The following paper presents the development of an algorithm, in charge of detecting, classifying and grabbing occluded objects, using artificial intelligence techniques, machine vision for the recognition of the environment, an anthropomorphic manipulator for the manipulation of the elements. 5 types of tools were used for their detection and classification, where the user selects one of them, so that the program searches for it in the work environment and delivers it in a specific area, overcoming difficulties such as occlusions of up to 70%. These tools were classified using two CNN (convolutional neural network) type networks, a fast R-CNN (fast region-based CNN) for the detection and classification of occlusions, and a DAG-CNN (directed acyclic graph-CNN) for the classification tools. Furthermore, a Haar classifier was trained in order to compare its ability to recognize occlusions with respect to the fast R-CNN. Fast R-CNN and DAG-CNN achieved 70.9% and 96.2% accuracy, respectively, Haar classifiers with about 50% accuracy, and an accuracy of grip and delivery of occluded objects of 90% in the application, was achieved.
Traffic Violation Detection Using Multiple Trajectories of VehiclesIJERA Editor
In general lane change violations are likely to happen before the stop line in the red-light violation detection
region. The system which can be detecting red-light and lane change violation is very useful for the traffic
management detection using vehicles moving in the region of interest and combining with the evaluation of the
trajectories behavior of multiple vehicles using mean square displacement (MSD) to detected both of violation.
We are using image processing technique only to detected traffic signal without help of another other system.
The experiment result shows that the algorithm is high accuracy to detect both of violation.
Here are the key motivations and objectives of this project:
- The current solutions for bin picking are limited to planar object arrangements. This project aims to address bin picking for 3D cluttered arrangements using additional sensor data.
- Automating object picking is important for applications where human intervention is not possible due to unsafe environments like nuclear reactors.
- Previous approaches using stereo vision have limitations for featureless objects where correspondence between images is difficult. This project proposes using monocular vision with laser range finder data to estimate object pose.
- The goal is to develop an autonomous robotic system that can accurately locate and pick objects from a cluttered 3D bin using sensor fusion from a camera and laser range finder. This will help autom
A Critical Survey on Detection of Object and Tracking of Object With differen...Editor IJMTER
Basically object detection and object tracking are two important and challenging aspects in
many computer vision applications like surveillance system, vehicle navigation, autonomous robot
navigation, compression of video etc. Object detection is first low level important task for any video
surveillance application. To detection of moving object is a challenging task. Tracking is required in
higher level applications that required the location and shape of object. There are three key steps in
video analysis: detection of interesting moving objects, tracking of such objects from frame to frame,
and analysis of object tracks to recognize their behavior. Object detection and tracking especially for
human and vehicle is currently most active research topic. A lot of research has been undergoing
ranging from applications to noble algorithms. The main objective of this paper is to review (survey)
of various moving object detection and object tracking methodologies.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScscpconf
1) The document presents a new technique for fault detection and isolation that uses neural networks to generate models of normal and faulty system behaviors. A decision tree is then used to evaluate residuals and isolate faults.
2) The technique is demonstrated on a benchmark process for an electro-pneumatic valve actuator. Neural networks are used to generate models of the actuator's normal and 19 possible faulty behaviors.
3) A decision tree structure is proposed to simplify online fault diagnosis by only evaluating the most significant residuals needed at each step to isolate faults. This reduces computational effort compared to evaluating all residuals.
Survey on video object detection & trackingijctet
This document summarizes previous work on video object detection and tracking techniques. It discusses research papers that used techniques like active contour modeling, gradient-based attraction fields, neural fuzzy networks, and region-based contour extraction for object tracking. Background subtraction, frame differencing, optical flow, spatio-temporal features, Kalman filtering, and contour tracking are described as common video object detection techniques. The challenges of multi-object data association and state estimation for tracking multiple objects are also mentioned.
Obstacle detection for autonomous systems using stereoscopic images and bacte...IJECEIAES
This paper presents a low cost strategy for real-time estimation of the position of ob- stacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.
IRJET-Vision Based Occupant Detection in Unattended VehicleIRJET Journal
This document proposes a vision-based method to detect and classify occupants inside an unattended vehicle using face recognition and motion-based classification. The system uses a camera mounted inside the vehicle to detect occupants in real-time at 30 frames per second with high accuracy under different lighting and weather conditions. It detects occupants in two steps - first detecting objects using background subtraction, then classifying objects as human or non-human using motion-based classification. The system aims to improve safety and comfort by monitoring occupants for applications like airbag deployment and climate control.
Traffic Violation Detection Using Multiple Trajectories of VehiclesIJERA Editor
In general lane change violations are likely to happen before the stop line in the red-light violation detection
region. The system which can be detecting red-light and lane change violation is very useful for the traffic
management detection using vehicles moving in the region of interest and combining with the evaluation of the
trajectories behavior of multiple vehicles using mean square displacement (MSD) to detected both of violation.
We are using image processing technique only to detected traffic signal without help of another other system.
The experiment result shows that the algorithm is high accuracy to detect both of violation.
Here are the key motivations and objectives of this project:
- The current solutions for bin picking are limited to planar object arrangements. This project aims to address bin picking for 3D cluttered arrangements using additional sensor data.
- Automating object picking is important for applications where human intervention is not possible due to unsafe environments like nuclear reactors.
- Previous approaches using stereo vision have limitations for featureless objects where correspondence between images is difficult. This project proposes using monocular vision with laser range finder data to estimate object pose.
- The goal is to develop an autonomous robotic system that can accurately locate and pick objects from a cluttered 3D bin using sensor fusion from a camera and laser range finder. This will help autom
A Critical Survey on Detection of Object and Tracking of Object With differen...Editor IJMTER
Basically object detection and object tracking are two important and challenging aspects in
many computer vision applications like surveillance system, vehicle navigation, autonomous robot
navigation, compression of video etc. Object detection is first low level important task for any video
surveillance application. To detection of moving object is a challenging task. Tracking is required in
higher level applications that required the location and shape of object. There are three key steps in
video analysis: detection of interesting moving objects, tracking of such objects from frame to frame,
and analysis of object tracks to recognize their behavior. Object detection and tracking especially for
human and vehicle is currently most active research topic. A lot of research has been undergoing
ranging from applications to noble algorithms. The main objective of this paper is to review (survey)
of various moving object detection and object tracking methodologies.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScscpconf
1) The document presents a new technique for fault detection and isolation that uses neural networks to generate models of normal and faulty system behaviors. A decision tree is then used to evaluate residuals and isolate faults.
2) The technique is demonstrated on a benchmark process for an electro-pneumatic valve actuator. Neural networks are used to generate models of the actuator's normal and 19 possible faulty behaviors.
3) A decision tree structure is proposed to simplify online fault diagnosis by only evaluating the most significant residuals needed at each step to isolate faults. This reduces computational effort compared to evaluating all residuals.
Survey on video object detection & trackingijctet
This document summarizes previous work on video object detection and tracking techniques. It discusses research papers that used techniques like active contour modeling, gradient-based attraction fields, neural fuzzy networks, and region-based contour extraction for object tracking. Background subtraction, frame differencing, optical flow, spatio-temporal features, Kalman filtering, and contour tracking are described as common video object detection techniques. The challenges of multi-object data association and state estimation for tracking multiple objects are also mentioned.
Obstacle detection for autonomous systems using stereoscopic images and bacte...IJECEIAES
This paper presents a low cost strategy for real-time estimation of the position of ob- stacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.
IRJET-Vision Based Occupant Detection in Unattended VehicleIRJET Journal
This document proposes a vision-based method to detect and classify occupants inside an unattended vehicle using face recognition and motion-based classification. The system uses a camera mounted inside the vehicle to detect occupants in real-time at 30 frames per second with high accuracy under different lighting and weather conditions. It detects occupants in two steps - first detecting objects using background subtraction, then classifying objects as human or non-human using motion-based classification. The system aims to improve safety and comfort by monitoring occupants for applications like airbag deployment and climate control.
New Approach for Detecting and Tracking a Moving Object IJECEIAES
This article presents the implementation of a tracking system for a moving target using a fixed camera. The objective of this work is the ability to detect a moving object and locate their positions. In picture processing, tracking moving objects in a known or unknown environment is commonly studied. It is based on invariance properties of objects of interest. The invariance can affect the geometry of the scene or the objects. The proposed approach is composed of several steps; the first is the extraction of points of interest in the current image. Then, these points will be tracked in the following image by using techniques for calculating the optical flow. After this step, the static points will be removed to focus on moving objects, That is to say, there is only the characteristic points belonging to moving objects. Now, to detect moving targets using images of the video, the background is first extracted from the successive images. In our approach, a method of the average values of every pixel has been developed for modeling background. The last step which stays before switching to tracking moving object is the segmentation which allows identifying every moving object. And by using the characteristic points in the previous steps.
Development of a Condition Monitoring Algorithm for Industrial Robots based o...IJECEIAES
Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A NOVEL IMAGE SEGMENTATION ENHANCEMENT TECHNIQUE BASED ON ACTIVE CONTOUR AND...acijjournal
This document summarizes a novel image segmentation technique based on active contours and topological alignments. The technique aims to improve boundary detection by incorporating the advantages of both active contours and topological alignments. It presents a two-step algorithm: 1) Initial segmentation is performed using topological alignments to improve cell tracking results. 2) The output is transformed into the input for an active contour model, which evolves toward cell boundaries for analysis of cell mobility. Tests on 70 grayscale cell images showed the technique achieved better segmentation and boundary detection compared to active contours alone, including for low contrast images and cases of under/over-segmentation.
MRI Brain Tumour Classification Using SURF and SIFT FeaturesIJMTST Journal
The features of an image are very important to classify different images. The classification of images is
done by feature extraction using Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform
(SIFT) methods for extraction. SIFT method is used to detect the images with larger corners and extract them.
SURF, the name itself represents a speed method to extract the features when compared to SIFT. KNN
classifier is used to classify the images based on the features extracted from both techniques. So these
combined processes are applied to classify tumour and non-tumour images more accurately.
This document proposes a hybrid approach using genetic algorithm, K-nearest neighbor, and probabilistic neural network for classifying MRI brain tumors. It extracts texture features using gray level co-occurrence matrix from wavelet decomposed MRI images. A genetic algorithm is then used for feature selection to identify an optimal feature subset for classification. Finally, probabilistic neural network is used to classify tumors into seven types based on the selected features, achieving accurate classification results.
A Fast Laser Motion Detection and Approaching Behavior Monitoring Method for ...toukaigi
1. The document describes a method for a Moving Object Alarm System (MOAS) that uses a laser sensor to detect moving objects, monitor their trajectories and approaching speed, and provide alerts if approaching in a dangerous manner.
2. The method defines a boundary around the monitored area and uses a fan-shaped grid to efficiently detect continuous moving objects. Object association across time is determined by updating a deviation matrix measuring changes in range, angle, and size of detected objects.
3. Outdoor experiments tested passing, approaching, and crossing objects, finding the method effectively detected motion and monitored approaching behavior in real-time.
AUTOMATED MANAGEMENT OF POTHOLE RELATED DISASTERS USING IMAGE PROCESSING AND ...ijcsit
Potholes though seem inconsequential, may cause accidents resulting in loss of human life. In this paper, we present an automated system to efficiently manage the potholes in a ward by deploying geotagging and image processing techniques that overcomes the drawbacks associated with the existing
survey-oriented systems. Image processing is used for identification of target pothole regions in the 2D
images using edge detection and morphological image processing operations. A method is developed to
accurately estimate the dimensions of the potholes from their images, analyze their area and depth,estimate the quantity of filling material required and therefore enabling pothole attendance on a priority basis. This will further enable the government official to have a fully automated system for e f f e c t i v e l y ma n a g i ng pothole related disasters.
Machinery signal separation using non-negative matrix factorization with real...journalBEEI
A big challenge in detecting damage occurs when the sound of a machine mixes with the sound of another machine. This paper proposes the separation of mixed acoustic signals using Non-negative Matrix Factorization (NMF) method for fault diagnosis. The NMF method is an effective solution for finding hidden parameters when the number of observations obtained by the sensor is less than the number of sources. The real mixing process is done by placing two microphones in front of the machine. Two microphones will be used as sensors to capture a mixture of four machinery signals. Performance testing of signal separation is done by comparing baseline signals with estimated signals through the mean log spectral distance (LSD) and the mean square error (MSE). The smallest spectral distance between the estimated signal and the baseline signal is found in Ŝ2 with an average LSD of 1.26. The estimated signal Ŝ2 is the closest to the baseline signal with MSE of 1.15 x 10-2. The pattern of bearing damage in the male screw compressor can be identified from the spectrum of estimated signal through harmonic frequencies as in the estimated signal Ŝ3 which is seen at 11x fundamental frequency, 12x fundamental frequency, 15x fundamental frequency, and 16x fundamental frequency.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A NOVEL METHOD FOR PERSON TRACKING BASED K-NN : COMPARISON WITH SIFT AND MEAN...sipij
Object tracking can be defined as the process of detecting an object of interest from a video scene and
keeping track of its motion, orientation, occlusion etc. in order to extract useful information. It is indeed a
challenging problem and it’s an important task. Many researchers are getting attracted in the field of
computer vision, specifically the field of object tracking in video surveillance. The main purpose of this
paper is to give to the reader information of the present state of the art object tracking, together with
presenting steps involved in Background Subtraction and their techniques. In related literature we found
three main methods of object tracking: the first method is the optical flow; the second is related to the
background subtraction, which is divided into two types presented in this paper, then the temporal
differencing and the SIFT method and the last one is the mean shift method. We present a novel approach
to background subtraction that compare a current frame with the background model that we have set
before, so we can classified each pixel of the image as a foreground or a background element, then comes
the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is
divided into two different methods, the surface method and the K-NN method, both are explained in the
paper. Our proposed method is implemented and evaluated using CAVIAR database.
A New Algorithm for Tracking Objects in Videos of Cluttered ScenesZac Darcy
The work presented in this paper describes a novel algorithm for automatic video object tracking based on
a process of subtraction of successive frames, where the prediction of the direction of movement of the
object being tracked is carried out by analyzing the changing areas generated as result of the object’s
motion, specifically in regions of interest defined inside the object being tracked in both the current and the
next frame. Simultaneously, it is initiated a minimization process which seeks to determine the location of
the object being tracked in the next frame using a function which measures the grade of dissimilarity
between the region of interest defined inside the object being tracked in the current frame and a moving
region in a next frame. This moving region is displaced in the direction of the object’s motion predicted on
the process of subtraction of successive frames. Finally, the location of the moving region of interest in the
next frame that minimizes the proposed function of dissimilarity corresponds to the predicted location of
the object being tracked in the next frame. On the other hand, it is also designed a testing platform which is
used to create virtual scenarios that allow us to assess the performance of the proposed algorithm. These
virtual scenarios are exposed to heavily cluttered conditions where areas which surround the object being
tracked present a high variability. The results obtained with the proposed algorithm show that the tracking
process was successfully carried out in a set of virtual scenarios under different challenging conditions.
AN ENHANCED BLOCK BASED EDGE DETECTION TECHNIQUE USING HYSTERESIS THRESHOLDING sipij
Edge detection is a crucial step in various image processing systems like computer vision , pattern
recognition and feature extraction. The Canny edge detection algorithm even though exhibits high
accuracy, is computationally more complex compared to other edge detection techniques. A block based
distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for
edge detection depending on block type and the distribution of gradients in each block. A novel method of
computation of high threshold has been proposed in this paper. Block-based hysteresis thresholds are
computed using a non uniform gradient magnitude histogram. The algorithm exhibits remarkably high
edge detection accuracy, scalability and significantly reduced computational time. Pratt’s Figure of Merit
quantifies the accuracy of the edge detector, which showed better values than that of original Canny and
distributed Canny edge detector for benchmark dataset. The method detected all visually prominent edges
for diverse block size.
UNSUPERVISED ROBOTIC SORTING: TOWARDS AUTONOMOUS DECISION MAKING ROBOTSijaia
This document presents research on unsupervised robotic sorting (URS). The researchers propose a solution to the URS problem where unknown objects are sorted into groups without predefined classes. Their approach uses convolutional neural networks (CNNs) to extract features from images of each object, then applies clustering algorithms to group similar objects. They test their image clustering pipeline on various datasets and propose a new dataset to evaluate robustness under different conditions. Additionally, they introduce a multi-view clustering approach using ensemble clustering with multiple images of each object to increase sorting robustness. The paper formalizes URS as a new problem and presents the first application of image-set clustering to robotics decision making.
CELL TRACKING QUALITY COMPARISON BETWEEN ACTIVE SHAPE MODEL (ASM) AND ACTIVE ...ijitcs
The aim of this paper is to introduce a comparison between cell tracking using active shape model (ASM)
and active appearance model (AAM) algorithms, to compare the cells tracking quality between the two
methods to track the mobility of the living cells. Where sensitive and accurate cell tracking system is
essential to cell motility studies. The active shape model (ASM) and active appearance model (AAM)
algorithms has proved to be a successful methods for matching statistical models. The experimental results
indicate the ability of (AAM) meth
Under the certain circumstances of the low and unacceptable accuracy on image recognition, the feature
extraction method for optical images based on the wavelet space feature spectrum entropy is recently
studied. With this method, the principle that the energy is constant before and after the wavelet
transformation is employed to construct the wavelet energy pattern matrices, and the feature spectrum
entropy of singular value is extracted as the image features by singular value decomposition of the matrix.
At the same time, BP neural network is also applied in image recognition. The experimental results show
that high image recognition accuracy can be acquired by using the feature extraction method for optical
images proposed in this paper, which proves the validity of the method.
Object gripping algorithm for robotic assistance by means of deep leaning IJECEIAES
This document presents a new algorithm using deep learning techniques for object gripping by a robot. It uses a Faster R-CNN to classify and locate three types of objects (cylinder, parallelepiped, toroid) with 100% accuracy. It also uses a CNN for regression to estimate the rotation angle of the parallelepiped with a mean error of 0.769 degrees. Testing in a virtual environment showed the algorithm could classify objects and grip them successfully at 5 frames per second using a three-finger gripper for increased stability over a two-finger gripper.
Robotic navigation algorithm with machine vision IJECEIAES
In the field of robotics, it is essential to know the work area in which the agent is going to develop, for that reason, different methods of mapping and spatial location have been developed for different applications. In this article, a machine vision algorithm is proposed, which is responsible for identifying objects of interest within a work area and determining the polar coordinates to which they are related to the observer, applicable either with a fixed camera or in a mobile agent such as the one presented in this document. The developed algorithm was evaluated in two situations, determining the position of six objects in total around the mobile agent. These results were compared with the real position of each of the objects, reaching a high level of accuracy with an average error of 1.3271% in the distance and 2.8998% in the angle.
Paper biological risk detection through deep learning and fuzzy systemIJECEIAES
Given the recent events worldwide due to viral diseases that affect human health, automatic monitoring systems are one of the strong points of research that has gained strength, where the detection of biohazardous waste of a sanitary nature is highlighted related to viral diseases stands out. It is essential in this field to generate developments aimed at saving lives, where robotic systems can operate as assistants in various fields. In this work an artificial intelligence algorithm based on two stages is presented, one is the recognition of paper debris using a ResNet-50, chosen for its object localization capacity, and the other is a fuzzy inference system for the generation of alarm states due to biological risk by such debris, where fuzzy logic helps to establish a model for a non-predictive system as the one exposed. A biohazard detection algorithm for paper waste is described, oriented to operate on an assistive robot in a residential environment. The training parameters of the network, which achieve 100% accuracy with confidence levels between 82% for very small waste and 100% in direct view, are presented. Timing cycles are established for validation of the exposure time of the waste, where through the fuzzy system, risk alarms are generated, which allows establishing a system with an average reliability of 98%.
Real Human Face Detection for Surveillance System Using Heterogeneous Sensors csandit
This document describes a new method for real-time human face detection using heterogeneous sensors. The method uses classifiers trained on data from both image and other sensors to detect faces. It aims to improve accuracy while reducing processing time by using a minimum number of cascading classifiers. Sensors are synchronized so data from different sources can be processed simultaneously. Experimental results show the new approach enhances accuracy in detecting real humans over methods using only image data. It also improves speed by reducing the number of classifiers needed.
REAL HUMAN FACE DETECTION FOR SURVEILLANCE SYSTEM USING HETEROGENEOUS SENSORS cscpconf
This document describes a new method for real-time human face detection using heterogeneous sensors. The method uses classifiers trained on data from both image and other sensors to detect faces. It aims to improve accuracy while reducing processing time by using a minimum number of cascading classifiers. Sensors are synchronized so data from different sources can be processed simultaneously. Experimental results show the new approach enhances accuracy in detecting real humans over methods using only image data. It also improves speed by reducing the number of classifiers needed.
This article presents a work oriented to assistive robotics, where a scenario is established for a robot to reach a tool in the hand of a user when they have verbally requested it by his name. For this, three convolutional neural networks are trained, one for recognition of a group of tools, which obtained an accuracy of 98% identifying the tools established for the application, that are scalpel, screwdriver and scissors; one for speech recognition, trained with the names of the tools in Spanish language, where its validation accuracy reaches a 97.5% in the recognition of the words; and another for recognition of the user's hand, taking in consideration the classification of 2 gestures: Open and Closed hand, where a 96.25% accuracy was achieved. With those networks, tests in real-time are performed, presenting results in the delivery of each tool with a 100% of accuracy, i.e. the robot was able to identify correctly what the user requested, recognize correctly each tool and deliver the one need when the user opened their hand, taking an average time of 45 seconds in the execution of the application.
New Approach for Detecting and Tracking a Moving Object IJECEIAES
This article presents the implementation of a tracking system for a moving target using a fixed camera. The objective of this work is the ability to detect a moving object and locate their positions. In picture processing, tracking moving objects in a known or unknown environment is commonly studied. It is based on invariance properties of objects of interest. The invariance can affect the geometry of the scene or the objects. The proposed approach is composed of several steps; the first is the extraction of points of interest in the current image. Then, these points will be tracked in the following image by using techniques for calculating the optical flow. After this step, the static points will be removed to focus on moving objects, That is to say, there is only the characteristic points belonging to moving objects. Now, to detect moving targets using images of the video, the background is first extracted from the successive images. In our approach, a method of the average values of every pixel has been developed for modeling background. The last step which stays before switching to tracking moving object is the segmentation which allows identifying every moving object. And by using the characteristic points in the previous steps.
Development of a Condition Monitoring Algorithm for Industrial Robots based o...IJECEIAES
Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A NOVEL IMAGE SEGMENTATION ENHANCEMENT TECHNIQUE BASED ON ACTIVE CONTOUR AND...acijjournal
This document summarizes a novel image segmentation technique based on active contours and topological alignments. The technique aims to improve boundary detection by incorporating the advantages of both active contours and topological alignments. It presents a two-step algorithm: 1) Initial segmentation is performed using topological alignments to improve cell tracking results. 2) The output is transformed into the input for an active contour model, which evolves toward cell boundaries for analysis of cell mobility. Tests on 70 grayscale cell images showed the technique achieved better segmentation and boundary detection compared to active contours alone, including for low contrast images and cases of under/over-segmentation.
MRI Brain Tumour Classification Using SURF and SIFT FeaturesIJMTST Journal
The features of an image are very important to classify different images. The classification of images is
done by feature extraction using Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform
(SIFT) methods for extraction. SIFT method is used to detect the images with larger corners and extract them.
SURF, the name itself represents a speed method to extract the features when compared to SIFT. KNN
classifier is used to classify the images based on the features extracted from both techniques. So these
combined processes are applied to classify tumour and non-tumour images more accurately.
This document proposes a hybrid approach using genetic algorithm, K-nearest neighbor, and probabilistic neural network for classifying MRI brain tumors. It extracts texture features using gray level co-occurrence matrix from wavelet decomposed MRI images. A genetic algorithm is then used for feature selection to identify an optimal feature subset for classification. Finally, probabilistic neural network is used to classify tumors into seven types based on the selected features, achieving accurate classification results.
A Fast Laser Motion Detection and Approaching Behavior Monitoring Method for ...toukaigi
1. The document describes a method for a Moving Object Alarm System (MOAS) that uses a laser sensor to detect moving objects, monitor their trajectories and approaching speed, and provide alerts if approaching in a dangerous manner.
2. The method defines a boundary around the monitored area and uses a fan-shaped grid to efficiently detect continuous moving objects. Object association across time is determined by updating a deviation matrix measuring changes in range, angle, and size of detected objects.
3. Outdoor experiments tested passing, approaching, and crossing objects, finding the method effectively detected motion and monitored approaching behavior in real-time.
AUTOMATED MANAGEMENT OF POTHOLE RELATED DISASTERS USING IMAGE PROCESSING AND ...ijcsit
Potholes though seem inconsequential, may cause accidents resulting in loss of human life. In this paper, we present an automated system to efficiently manage the potholes in a ward by deploying geotagging and image processing techniques that overcomes the drawbacks associated with the existing
survey-oriented systems. Image processing is used for identification of target pothole regions in the 2D
images using edge detection and morphological image processing operations. A method is developed to
accurately estimate the dimensions of the potholes from their images, analyze their area and depth,estimate the quantity of filling material required and therefore enabling pothole attendance on a priority basis. This will further enable the government official to have a fully automated system for e f f e c t i v e l y ma n a g i ng pothole related disasters.
Machinery signal separation using non-negative matrix factorization with real...journalBEEI
A big challenge in detecting damage occurs when the sound of a machine mixes with the sound of another machine. This paper proposes the separation of mixed acoustic signals using Non-negative Matrix Factorization (NMF) method for fault diagnosis. The NMF method is an effective solution for finding hidden parameters when the number of observations obtained by the sensor is less than the number of sources. The real mixing process is done by placing two microphones in front of the machine. Two microphones will be used as sensors to capture a mixture of four machinery signals. Performance testing of signal separation is done by comparing baseline signals with estimated signals through the mean log spectral distance (LSD) and the mean square error (MSE). The smallest spectral distance between the estimated signal and the baseline signal is found in Ŝ2 with an average LSD of 1.26. The estimated signal Ŝ2 is the closest to the baseline signal with MSE of 1.15 x 10-2. The pattern of bearing damage in the male screw compressor can be identified from the spectrum of estimated signal through harmonic frequencies as in the estimated signal Ŝ3 which is seen at 11x fundamental frequency, 12x fundamental frequency, 15x fundamental frequency, and 16x fundamental frequency.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A NOVEL METHOD FOR PERSON TRACKING BASED K-NN : COMPARISON WITH SIFT AND MEAN...sipij
Object tracking can be defined as the process of detecting an object of interest from a video scene and
keeping track of its motion, orientation, occlusion etc. in order to extract useful information. It is indeed a
challenging problem and it’s an important task. Many researchers are getting attracted in the field of
computer vision, specifically the field of object tracking in video surveillance. The main purpose of this
paper is to give to the reader information of the present state of the art object tracking, together with
presenting steps involved in Background Subtraction and their techniques. In related literature we found
three main methods of object tracking: the first method is the optical flow; the second is related to the
background subtraction, which is divided into two types presented in this paper, then the temporal
differencing and the SIFT method and the last one is the mean shift method. We present a novel approach
to background subtraction that compare a current frame with the background model that we have set
before, so we can classified each pixel of the image as a foreground or a background element, then comes
the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is
divided into two different methods, the surface method and the K-NN method, both are explained in the
paper. Our proposed method is implemented and evaluated using CAVIAR database.
A New Algorithm for Tracking Objects in Videos of Cluttered ScenesZac Darcy
The work presented in this paper describes a novel algorithm for automatic video object tracking based on
a process of subtraction of successive frames, where the prediction of the direction of movement of the
object being tracked is carried out by analyzing the changing areas generated as result of the object’s
motion, specifically in regions of interest defined inside the object being tracked in both the current and the
next frame. Simultaneously, it is initiated a minimization process which seeks to determine the location of
the object being tracked in the next frame using a function which measures the grade of dissimilarity
between the region of interest defined inside the object being tracked in the current frame and a moving
region in a next frame. This moving region is displaced in the direction of the object’s motion predicted on
the process of subtraction of successive frames. Finally, the location of the moving region of interest in the
next frame that minimizes the proposed function of dissimilarity corresponds to the predicted location of
the object being tracked in the next frame. On the other hand, it is also designed a testing platform which is
used to create virtual scenarios that allow us to assess the performance of the proposed algorithm. These
virtual scenarios are exposed to heavily cluttered conditions where areas which surround the object being
tracked present a high variability. The results obtained with the proposed algorithm show that the tracking
process was successfully carried out in a set of virtual scenarios under different challenging conditions.
AN ENHANCED BLOCK BASED EDGE DETECTION TECHNIQUE USING HYSTERESIS THRESHOLDING sipij
Edge detection is a crucial step in various image processing systems like computer vision , pattern
recognition and feature extraction. The Canny edge detection algorithm even though exhibits high
accuracy, is computationally more complex compared to other edge detection techniques. A block based
distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for
edge detection depending on block type and the distribution of gradients in each block. A novel method of
computation of high threshold has been proposed in this paper. Block-based hysteresis thresholds are
computed using a non uniform gradient magnitude histogram. The algorithm exhibits remarkably high
edge detection accuracy, scalability and significantly reduced computational time. Pratt’s Figure of Merit
quantifies the accuracy of the edge detector, which showed better values than that of original Canny and
distributed Canny edge detector for benchmark dataset. The method detected all visually prominent edges
for diverse block size.
UNSUPERVISED ROBOTIC SORTING: TOWARDS AUTONOMOUS DECISION MAKING ROBOTSijaia
This document presents research on unsupervised robotic sorting (URS). The researchers propose a solution to the URS problem where unknown objects are sorted into groups without predefined classes. Their approach uses convolutional neural networks (CNNs) to extract features from images of each object, then applies clustering algorithms to group similar objects. They test their image clustering pipeline on various datasets and propose a new dataset to evaluate robustness under different conditions. Additionally, they introduce a multi-view clustering approach using ensemble clustering with multiple images of each object to increase sorting robustness. The paper formalizes URS as a new problem and presents the first application of image-set clustering to robotics decision making.
CELL TRACKING QUALITY COMPARISON BETWEEN ACTIVE SHAPE MODEL (ASM) AND ACTIVE ...ijitcs
The aim of this paper is to introduce a comparison between cell tracking using active shape model (ASM)
and active appearance model (AAM) algorithms, to compare the cells tracking quality between the two
methods to track the mobility of the living cells. Where sensitive and accurate cell tracking system is
essential to cell motility studies. The active shape model (ASM) and active appearance model (AAM)
algorithms has proved to be a successful methods for matching statistical models. The experimental results
indicate the ability of (AAM) meth
Under the certain circumstances of the low and unacceptable accuracy on image recognition, the feature
extraction method for optical images based on the wavelet space feature spectrum entropy is recently
studied. With this method, the principle that the energy is constant before and after the wavelet
transformation is employed to construct the wavelet energy pattern matrices, and the feature spectrum
entropy of singular value is extracted as the image features by singular value decomposition of the matrix.
At the same time, BP neural network is also applied in image recognition. The experimental results show
that high image recognition accuracy can be acquired by using the feature extraction method for optical
images proposed in this paper, which proves the validity of the method.
Object gripping algorithm for robotic assistance by means of deep leaning IJECEIAES
This document presents a new algorithm using deep learning techniques for object gripping by a robot. It uses a Faster R-CNN to classify and locate three types of objects (cylinder, parallelepiped, toroid) with 100% accuracy. It also uses a CNN for regression to estimate the rotation angle of the parallelepiped with a mean error of 0.769 degrees. Testing in a virtual environment showed the algorithm could classify objects and grip them successfully at 5 frames per second using a three-finger gripper for increased stability over a two-finger gripper.
Robotic navigation algorithm with machine vision IJECEIAES
In the field of robotics, it is essential to know the work area in which the agent is going to develop, for that reason, different methods of mapping and spatial location have been developed for different applications. In this article, a machine vision algorithm is proposed, which is responsible for identifying objects of interest within a work area and determining the polar coordinates to which they are related to the observer, applicable either with a fixed camera or in a mobile agent such as the one presented in this document. The developed algorithm was evaluated in two situations, determining the position of six objects in total around the mobile agent. These results were compared with the real position of each of the objects, reaching a high level of accuracy with an average error of 1.3271% in the distance and 2.8998% in the angle.
Paper biological risk detection through deep learning and fuzzy systemIJECEIAES
Given the recent events worldwide due to viral diseases that affect human health, automatic monitoring systems are one of the strong points of research that has gained strength, where the detection of biohazardous waste of a sanitary nature is highlighted related to viral diseases stands out. It is essential in this field to generate developments aimed at saving lives, where robotic systems can operate as assistants in various fields. In this work an artificial intelligence algorithm based on two stages is presented, one is the recognition of paper debris using a ResNet-50, chosen for its object localization capacity, and the other is a fuzzy inference system for the generation of alarm states due to biological risk by such debris, where fuzzy logic helps to establish a model for a non-predictive system as the one exposed. A biohazard detection algorithm for paper waste is described, oriented to operate on an assistive robot in a residential environment. The training parameters of the network, which achieve 100% accuracy with confidence levels between 82% for very small waste and 100% in direct view, are presented. Timing cycles are established for validation of the exposure time of the waste, where through the fuzzy system, risk alarms are generated, which allows establishing a system with an average reliability of 98%.
Real Human Face Detection for Surveillance System Using Heterogeneous Sensors csandit
This document describes a new method for real-time human face detection using heterogeneous sensors. The method uses classifiers trained on data from both image and other sensors to detect faces. It aims to improve accuracy while reducing processing time by using a minimum number of cascading classifiers. Sensors are synchronized so data from different sources can be processed simultaneously. Experimental results show the new approach enhances accuracy in detecting real humans over methods using only image data. It also improves speed by reducing the number of classifiers needed.
REAL HUMAN FACE DETECTION FOR SURVEILLANCE SYSTEM USING HETEROGENEOUS SENSORS cscpconf
This document describes a new method for real-time human face detection using heterogeneous sensors. The method uses classifiers trained on data from both image and other sensors to detect faces. It aims to improve accuracy while reducing processing time by using a minimum number of cascading classifiers. Sensors are synchronized so data from different sources can be processed simultaneously. Experimental results show the new approach enhances accuracy in detecting real humans over methods using only image data. It also improves speed by reducing the number of classifiers needed.
This article presents a work oriented to assistive robotics, where a scenario is established for a robot to reach a tool in the hand of a user when they have verbally requested it by his name. For this, three convolutional neural networks are trained, one for recognition of a group of tools, which obtained an accuracy of 98% identifying the tools established for the application, that are scalpel, screwdriver and scissors; one for speech recognition, trained with the names of the tools in Spanish language, where its validation accuracy reaches a 97.5% in the recognition of the words; and another for recognition of the user's hand, taking in consideration the classification of 2 gestures: Open and Closed hand, where a 96.25% accuracy was achieved. With those networks, tests in real-time are performed, presenting results in the delivery of each tool with a 100% of accuracy, i.e. the robot was able to identify correctly what the user requested, recognize correctly each tool and deliver the one need when the user opened their hand, taking an average time of 45 seconds in the execution of the application.
This document provides an overview of recent developments in object detection using AI robots. It explores several deep learning-based object detection techniques and their advantages over traditional computer vision methods. The paper discusses how object detection is used in robotics applications like grasping, manipulating, and navigation. It also presents the results of experiments conducted to evaluate an object detection system using a robot equipped with cameras and sensors. The system uses a Fast R-CNN algorithm combined with a Kalman filter for real-time object detection and tracking.
Visual control system for grip of glasses oriented to assistance robotics IJECEIAES
Assistance robotics is presented as a means of improving the quality of life of people with disabilities, an application case is presented in assisted feeding. This paper presents the development of a system based on artificial intelligence techniques, for the grip of a glass, so that it does not slip during its manipulation by means of a robotic arm, as the liquid level varies. A faster R-CNN is used for the detection of the glass and the arm's gripper, and from the data obtained by the network, the mass of the beverage is estimated, and a delta of distance between the gripper and the liquid. These estimated values are used as inputs for a fuzzy system which has as output the torque that the motor that drives the gripper must exert. It was possible to obtain a 97.3% accuracy in the detection of the elements of interest in the environment with the faster R-CNN, and a 76% performance in the grips of the glass through the fuzzy algorithm.
Objects detection and tracking using fast principle component purist and kalm...IJECEIAES
The detection and tracking of moving objects attracted a lot of concern because of the vast computer vision applications. This paper proposes a new algorithm based on several methods for identifying, detecting, and tracking an object in order to develop an effective and efficient system in several applications. This algorithm has three main parts: the first part for background modeling and foreground extraction, the second part for smoothing, filtering and detecting moving objects within the video frame and the last part includes tracking and prediction of detected objects. In this proposed work, a new algorithm to detect moving objects from video data is designed by the Fast Principle Component Purist (FPCP). Then we used an optimal filter that performs well to reduce noise through the median filter. The Fast Regionconvolution neural networks (Fast- RCNN) is used to add smoothness to the spatial identification of objects and their areas. Then the detected object is tracked by Kalman Filter. Experimental results show that our algorithm adapts to different situations and outperforms many existing algorithms.
Fast channel selection method using modified camel travelling behavior algorithmTELKOMNIKA JOURNAL
Brain computer interface (BCI) is a protocol to communicate between the human brain and a device or application using brain signals. These signals translated to useful commands by using features extraction and classification. The most widely used features is the power of alpha and beta rhythms. This type of features gives only 70% of classification accuracy without any extra processing using fixed channels to read the signals. Because the distribution of the power in the brain is not a standard for all people, each one has his own brain power map. A selection algorithm is used to find the best channels that could generate higher power than the fixed ones. Modified camel travelling behavior algorithm is used to select the channels that used to extract the power of alpha and beta bands of motor imagery signals. This algorithm is faster to find the best set of channels, and obtain classification accuracy more than 95% using support vector machine classifier.
HUMAN BODY DETECTION AND SAFETY CARE SYSTEM FOR A FLYING ROBOTcsandit
Image-processing is one the challenging issue in robotic as well as electrical engineering
research contexts. This study proposes a system for extract and tracking objects by a
quadcopter’s flying robot and how to extract the human body. It is observed in image taken
from real-time camera that is embedded bottom of the quadcopter, there is a variance in human
behaviour being tracked or recorded such as position and, size, of the human. In the regard, the
paper tries to investigate an image-processing method for tracking humans’ body, concurrently.
For this process, an extraction method, which defines features to distinguish a human body, is
proposed. The proposed method creates a virtual shape of bodies for recognizing the body of
humans, also, generate an extractor according to its edge information. This method shows
better performance in term of precision as well as speed experimentally.
Visual victim detection and quadrotor-swarm coordination control in search an...IJECEIAES
We propose a distributed victim-detection algorithm through visual information on quadrotors using convolutional neuronal networks (CNN) in a search and rescue environment. Describing the navigation algorithm, which allows quadrotors to avoid collisions. Secondly, when one quadrotor detects a possible victim, it causes its closest neighbors to disconnect from the main swarm and form a new sub-swarm around the victim, which validates the victim’s status. Thus, a formation control that permits to acquire information is performed based on the well-known rendezvous consensus algorithm. Finally, images are processed using CNN identifying potential victims in the area. Given the uncertainty of the victim detection measurement among quadrotors’ cameras in the image processing, estimation consensus (EC) and max-estimation consensus (M-EC) algorithms are proposed focusing on agreeing over the victim detection estimation. We illustrate that M-EC delivers better results than EC in scenarios with poor visibility and uncertainty produced by fire and smoke. The algorithm proves that distributed fashion can obtain a more accurate result in decision-making on whether or not there is a victim, showing robustness under uncertainties and wrong measurements in comparison when a single quadrotor performs the mission. The well-functioning of the algorithm is evaluated by carrying out a simulation using V-Rep.
IRJET- Surveillance of Object Motion Detection and Caution System using B...IRJET Journal
This document describes a proposed surveillance system using a block matching algorithm for motion detection. The system would use IP cameras to stream video that is monitored for unauthorized activity. Motion detection is performed by comparing frames using the block matching algorithm to detect changes in pixel intensity values, which would trigger an alarm. The block matching algorithm divides frames into blocks of pixels and validates the maximum and minimum intensity of each pixel. Comparing blocks between frames identifies motion if intensity values change beyond a threshold. If motion is detected in a designated sensitive area, the system saves the video and sends alerts by email and mobile notification to users.
One of the most convenient biometrics approaches for identifying a person is dorsal hand veins recognition. In recent years, the dorsal hand veins have acquired increasing attention because of its characteristics such as universal, unique, permanent, contactless, and difficulty of forging, also, the veins remain unchanged when a human being grows. The captured dorsal hand veins image suffers from the many differences in lighting conditions, brightness, existing hair, and amount of noise. To solve these problems, this paper aims to extract and recognize dorsal hand veins based on the largest correlation coefficient. The proposed system consists of three stages: 1) preprocessing the image, 2) feature extraction, and 3) matching. In order to evaluate the proposed system performance, two databases have been employed. The test results illustrate the correct recognition rate (CRR), and accuracy of the first database are 99.38% and 99.46%, respectively, whereas the CRR, and accuracy of the second database are 99.11% and 99.07% respectively. As a result, we conclude that our proposed method for recognizing dorsal hand veins is feasible and effective.
This document describes a MEMS accelerometer-based system for hand gesture recognition. The system uses a portable device containing a triaxial accelerometer, microcontroller, and wireless transmission module. Users can write digits and make gestures, and the accelerometer measures the motions. A trajectory recognition algorithm processes the acceleration signals through preprocessing, feature generation/selection, and classification. The algorithm aims to accurately recognize gestures with high recognition rates. An experiment tests the algorithm on handwritten digit recognition with promising results.
This document summarizes research on developing a portable device using a MEMS accelerometer for hand gesture recognition. The device consists of a triaxial accelerometer, microcontroller, and wireless transmission module. It measures acceleration signals from hand motions and transmits them to a computer for trajectory recognition using a recognition algorithm. The algorithm processes the acceleration data through steps like filtering, feature extraction, and classification to identify gestures and enable control of electronic devices through hand motions. The research aims to create an accurate, low-cost gesture recognition system using a single accelerometer without additional sensors.
Machine learning based augmented reality for improved learning application th...IJECEIAES
Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-ofthe-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience.
A Robot Collision Avoidance Method Using Kinect and Global VisionTELKOMNIKA JOURNAL
This document describes a robot collision avoidance method using Kinect and global vision. Global vision is installed above the robot to detect potential collision objects in real-time using background subtraction and thresholding algorithms. Kinect detects human skeletons and uses a Kalman filter to predict joint positions over time. The predicted joint positions are used to calculate a human joint danger index to evaluate collision risk levels. Different motion control strategies are then applied depending on the obstacle category and danger index value.
Improving of Fingerprint Segmentation Images Based on K-MEANS and DBSCAN Clus...IJECEIAES
Nowadays, the fingerprint identification system is the most exploited sector of biometric. Fingerprint image segmentation is considered one of its first processing stage. Thus, this stage affects typically the feature extraction and matching process which leads to fingerprint recognition system with high accuracy. In this paper, three major steps are proposed. First, Soble and TopHat filtering method have been used to improve the quality of the fingerprint images. Then, for each local block in fingerprint image, an accurate separation of the foreground and background region is obtained by K-means clustering for combining 5-dimensional characteristics vector (variance, difference of mean, gradient coherence, ridge direction and energy spectrum). Additionally, in our approach, the local variance thresholding is used to reduce computing time for segmentation. Finally, we are combined to our system DBSCAN clustering which has been performed in order to overcome the drawbacks of K-means classification in fingerprint images segmentation. The proposed algorithm is tested on four different databases. Experimental results demonstrate that our approach is significantly efficacy against some recently published techniques in terms of separation between the ridge and non-ridge region.
HUMAN BODY DETECTION AND SAFETY CARE SYSTEM FOR A FLYING ROBOTcscpconf
Image-processing is one the challenging issue in robotic as well as electrical engineering research contexts. This study proposes a system for extract and tracking objects by a quadcopter’s flying robot and how to extract the human body. It is observed in image taken from real-time camera that is embedded bottom of the quadcopter, there is a variance in human behaviour being tracked or recorded such as position and, size, of the human. In the regard, the paper tries to investigate an image-processing method for tracking humans’ body, concurrently .For this process, an extraction method, which defines features to istinguish a human body, is proposed. The proposed method creates a virtual shape of bodies for recognizing the body of
humans, also, generate an extractor according to its edge information. This method shows better performance in term of precision as well as speed experimentally.
Similar to Algorithm of detection, classification and gripping of occluded objects by CNN techniques and Haar classifiers (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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of each person from their thermal detection, and define the presence of occlusions according to the results of
the depth information, to generate a free path of obstacles for a mobile robot.
CNN's are designed to perform recognition of desired patterns and characteristics in images, to classify
them into a specific category, as mentioned in [8, 9]. The CNN is composed of convolutional filters whose
parameters are trained with a database that has images or references of the patterns to be classified, which allow
the network to learn and extract the most relevant characteristics of an image, whose information is used to
generate a classification, as explained in [10], but not only images like is exposed in [11].
Some of the recently developed CNN applications are: 1) a 34-layer CNN led to the detection of
a wide range of cardiac arrhythmias from electrocardiograms, whose performance exceeded the average
results of Medical prediction, made by a group of 6 cardiologists [12]; 2) a trained cascade of CNNs to detect
and classify faces in a real environment, overcoming difficulties such as pose changes, expression, and
lighting, avoiding becoming computationally expensive [13]; 3) expanded use of CNNs towards a three-
dimensional environment, where volumes of magnetic resonance voxel of the prostate were evaluated,
generating a segmentation of the entire volume, using only a fraction of processing time compared to other
methods previously used [14].
Besides CNN, other artificial intelligence methods have been developed for the classification of
patterns, such as the fast R-CNN [15] and the DAG-CNN [16, 17], in the first case is former a stage of
extraction of a Regions of Interest (ROIs) that is responsible for of detecting desired elements in the input
image, extracting them and entering them into a CNN for classification, as explained in [18], while the last
consists of a branched structure where each branch contains a sequence of convolutional layers whose filters
vary of dimension, to extract characteristics of greater and smaller size of the input image, and in the end to
unify the results to give a classification, as indicated in [19].
Some examples of applications for the fast R-CNN are reported in [20] and [21]. The first report
applied the Faster R-CNN for face detection and classification, to achieve a higher processing speed
concerning other methods of deep learning, and the second report a multi-class fruit detection using a robotic
vision system based on Faster RCNN. On the other hand, the DAG-CNN has been used in applications such
as those described in [22] and [23], where the first publication used the DAG-CNN for the estimation of age
in people of different genders and ethnicities, taking advantage of the extraction of characteristics at multiple
scales of said network, with an accuracy of around 80%, and the second publication used the DAG-CNN for
the classification of heartbeats from electrocardiogram images (ECG), achieving the identification of 15
different signals with 97.15% accuracy.
Apart from the CNNs, there are other methods of recognizing elements in images, such as Haar
classifiers, which train a series of weak cascading classifiers, which receive an input image and through
a sliding window they discard those areas that do not contain the desired element until, in the end,
the windows indicating the object of interest, are obtained, [24]. Examples of application of Haar classifiers
are reported in [25] and [26], where the first one carried out a process of real-time detection of cow nipples to
generate an automatic milking system, while the second developed a method of counting and detecting
the number of books stacked on shelves, where 96% recognition was achieved when testing the system on
a total of 20 shelves and 233 books.
For the following article, a system for detecting, classifying and grabbing occluded objects was
designed using a manipulator robot in a physical and virtual environment, where artificial intelligence
techniques were used for the process of detection and classification of elements in the work area, and
a Kinect V1 was used as the machine vision system. This work generates a fundamental contribution for
the increase of the automation of robotized processes within unknown environments, where a sequence of
detection and elimination of occlusions was established since it is not possible to dodge them employing
obstacle avoidance algorithms, but they must be removed to be able to hold the desired object.
The present article is divided into 4 main sections. In the first section, a brief introduction was made.
In the second section, the operation of the algorithm proposed for the elimination of occlusions is explained.
In the third section, carried out on the algorithm in a physical environment, to obtain the quality of
recognition and delivery of desired elements, in the presence of partial occlusions. Finally, in the last section,
a series of conclusions were established regarding the operation of the algorithm, according to the results
obtained during the tests.
2. RESEARCH METHOD
The following is the basic operation of the program for detecting, classifying and grabbing
occluded objects, using artificial intelligence techniques, and captured three-dimensional information by
Kinect V1 (see [27]). For this, section 2 was divided into 2 subsections, the first raises the physical working
conditions for the implementation of the application, the second describes the algorithm’s flow.
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2.2. Algorithm flow
Step 1 program initialization: The first step consists of the initialization of the variables to be used in
the course of the application, where imagD was defined as the vector that contains the number of pixels
occupied by the Grip Zone in the RGB image of the Kinect ([128 174] pixels), and the PFH variable
stores two matrices, each with 10 rows and 3 columns, where the first matrix saves the final positions for
the desired objects, and the second saves the final positions for the unwanted elements (occlusions).
Step 2 tool detection: In this step, the first detection of tools in the environment is performed, where
it is determined whether the program is executed or terminated according to what is identified in
the Grip Zone as shown in Figure 1. Two methods of object detection, the fast R-CNN, and the Haar
classifiers, were trained in order to compare the accuracy and precision of detection of each, and thus define
which of the two is used in the algorithm. A database of 450 photos was established that were augmented
with the Data Augmentation program developed in [28], reaching a total of 3150 images, where 2800 were
used for training and 350 for testing. The detection boxes for both methods were defined as 55x28 pixels
since that is the maximum value of the space occupied by the tools in the image.
The fast R-CNN is a CNN-type neural network that has an ROI extraction stage designed for
the detection of elements in images, as detailed in [18]. The architecture of this network is defined in
the same way as that of a conventional CNN, using convolutional layers (CV), rectified linear units (RLU),
maxpool (MP), average pool, batch normalization (B), fully connected (FC), dropout (DO), softmax (SOFT),
among others, explained in [10]. The trained fast R-CNN has the architecture shown in Table 1, where it was
observed that, when using rectangular filters, 4 convolution layers, 300 training times and a MiniBatchSize
less than 30, the highest percentage of accuracy was achieved, receiving as input an image of image
dimensions ([128 174] pixels).
Table 1. Architecture of the fast R-CNN for the detection and classification of occluded tools
Layers CV+B+RLU CV+B+RLU MP CV+B+RLU CV+B+RLU FC1+RLU+DO FC2+RLU+DO
Filter Size 6x4 5x3 2x3 4x3 4x3 -- --
Stride 1 1 2 1 1 -- --
Number of Filters 16 128 -- 256 512 -- --
The results of the trained fast R-CNN are shown in Figure 3, where Figure 3(a) shows the confusion
matrix and Figure 3(b) shows the recall vs precision graph, with 1 being the free category, 2 being
the Occlusion category, and 3 the Background. The results obtained in the fast R-CNN were not ideal,
however, it was decided to use this network, since the structure proposed for the algorithm allows in step 5 to
recognize the tools that were not captured in Step 2, reducing the influence of the low accuracy of the Fast
R-CNN in the application, as described later in section 3.
For the training of the Haar classifiers, the values of a series of parameters that determine the basic
training characteristics, explained in [29], were adjusted. A Haar classifier was trained for the recognition of
the free category and another for the Occlusion category, whose accuracy and training parameters are shown
in Table 2, where the highest accuracy obtained was 42% and 48%, respectively. As explained in [22], Haar
classifiers need larger databases to obtain better detection results, however, the objective is to compare Haar
classifiers with fast R-CNN, the reason why the database was not increased and was used in both cases.
(a) (b)
Figure 3. Fast R-CNN: (a) confusion matrix and (b) recall vs precision
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Table 2. Training parameters of Haar classifiers
Parameter Accuracy
False
Alarm
Rate
Number
Cascade
Stages
Negative
Samples
Factor
True
Positive
Rate
Total
Positives
Samples
Total
Negative
Samples
Negative
Samples
Number
Positive
Samples
Free 42% 0.01 15 3 60% 2800 5000 1272 424
Occ 48% 0.01 100 3 60% 2800 5000 204 68
Step 3 tool classification: After the tool detection process, the recognized elements were extracted
from the image to classify them by means of a DAG-CNN in one of the 5 trained categories: Scalpel, Scissor,
Screwdriver, Spanner, Scissor and the result was compared with the desired object by the user, in order to
determine if it belongs to the group of desired objects or occlusions. A classification box of 70x70 pixels was
defined, the dimensions of which were taken from the upper left corner of the detection box generated in
Step 2 or Step 5, and 70 pixels to the right and 70 pixels down were captured. In Figure 4(a), the structure
used for the DAG-CNN of the application is shown, where a division of 2 branches that receive the input
image was generated and finally joined in a FC to classify, and in Figure 4(b) shows an example of
the database used for network training.
(a)
No interference
Pliers
1 interference
Scalpel
2 interference
Scissor
3 interference
Screwdriver
4 or more interference
Spanner
(b)
Figure 4. (a) Structure of and (b) database for the DAG-CNN
Table 3 reflected the architecture of the DAG-CNN, which was trained for 90 epochs,
with a database of 3000 training images and 300 test images, per category. Figure 5 shows the confusion
matrix of the DAG-CNN, which reached 96.2% accuracy, with a minimum of 91% accuracy per category.
The numbers correspond to the classification categories, where 1 is pliers, 2 is scalpel, 3 is scissor,
4 is screwdriver, and 5 is spanner. For example, the classification of Scalpel was one of the categories that
presented more difficulties in recognition, confusing itself with the Pliers category mainly.
Table 3. Architecture of the DAG-CNN (a) layers branch 1
CV+B
+RLU
MP
CV+B
+RLU
MP
CV+B
+RLU
MP
CV+B
+RLU
FC1+RLU+DO FC2+RLU+DO
Filter Size 4x4 3x3 3x3 3x3 3x3 3x3 3x3 -- --
Stride 1 2 1 2 1 2 1 -- --
Number of Filters 16 -- 128 -- 256 -- 512 -- --
Table 3. Architecture of the DAG-CNN (b) layers branch 2
CV+B
+RLU
MP
CV+B
+RLU
MP
CV+B
+RLU
MP FC1+RLU+DO FC2+RLU+DO
Filter Size 6x6 3x3 4x4 3x3 3x3 2x2 -- --
Stride 1 2 1 2 1 2 -- --
Number of Filters 16 -- 256 -- 512 -- -- --
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Figure 5. Confusion matrix for the DAG-CNN
Step 4 grip and delivery of tools: Once the algorithm defines whether the tool is a desired object or
an occlusion, the movement of the manipulator robot for the transfer of the element to the delivery zone is
executed. The center of the detection box of the tool classified as the holding point was selected, and the data
of the PFH variable was used to determine the delivery coordinates of the element. To detect the clamping
height of each tool concerning the table (Z coordinate) it was necessary to evaluate the three-dimensional
information of each detection box and calculate its average. The algorithm first removes the elements
classified as free, and then those defined as occlusion, changing the detection box only when all the elements
of the tool stack have been removed, which means that the calculated average height corresponds to
the height of the table.
Step 5 new tool detection: Before terminating the program, Step 5 was added, to verify
the presence of tools in the Grip Zone. This step is responsible for repeating the process of detection of
Step 2, through which the existence of new elements in the environment is defined, and according to
the results obtained, Step 3 and Step 4 are executed, or the program is terminated, as shown in Figure 1.
3. RESULTS AND ANALYSIS
The first comparison between the methods of detection and classification of occlusions mentioned
in section 2, where the percentages of accuracy, where the fast R-CNN exceeded the Haar classifiers by
20% accuracy. Additionally, a comparison was made between both methods applying them to the physical
work environment, with different percentages of occlusion. The fast R-CNN managed to recognize a greater
number of tools in the environment, and categorized them more accurately than the Haar classifiers, in
addition to presenting greater accuracy in the detection boxes. For these reasons, it was decided to use
the fast R-CNN in the algorithm.
The algorithm was applied in a previous simulation environment was tested by comparing
the number of desired tools moved to the delivery zone, with respect to the total number of desired tools
present in the Grip Zone, the results of which are shown in Table 4, where the column “Delivered tools”
shows the amount of Desired objects delivered (numerator) with respect to the total number of desired
objects present in the environment (denominator) for two fastening tests per tool, and the column
“Percentage” shows the percentages of successful deliveries for each case according to the results of
the column “Delivered tools”, in which the number of elements, their positions, the occlusion percentages,
and the objects desired by the user was varied.
According to the results of Table 4, the algorithm reached 80% accuracy in the process of detecting,
classifying and grabbing occluded objects, within a simulated environment, which means that at least 4 of 5
desired objects will be delivered correctly. During the tests, it was possible to observe two factors that
affected the accuracy of the algorithm: the low accuracy of the detection boxes generated by the fast R-CNN,
and the results of Step 5, which demonstrated inability to recognize any element in the simulated
environment, since there are no variations in the input image that allow new elements to be recognized.
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Table 4. Quality of delivery of desired objects of the simulated algorithm
Desired objects Delivered tools Percentage Average
Screwdriver 3/4 3/3 75% 100%
80%
Scalpel 3/5 2/2 60% 100%
Scissor 4/5 4/5 80% 80%
Pliers 5/5 1/2 100% 50%
Spanner 3/4 4/5 75% 80%
3.1. Functioning in the physical environment
To determine the performance of the algorithm within a physical environment, two premises were
established, the first focused on determining the percentage of tool stacks detected with respect to the total
amount of tool towers present in the Grip Zone (Detection Quality), and the second defined the percentage of
tools held correctly, with respect to the total amount of elements present in the environment (Holding
quality). Table 5 reflected the results obtained for both premises, where the “Stacked Tools” row indicated
the number of tools present in a stack of elements, and in the “Reinforcement Detection” row the percentage
of recognized tool stacks was recorded in the environment after executing Step 5 once.
Tests were carried out with stacks of elements that have between 1 and 5 tools, where 10 tests were
made for each quantity of stacked elements and varied from their positions to their orientations. The best
results of detection and clamping of tools were obtained for piles of elements between 1 and 3 tools, where
their average precision ranged between 80% and 90% while when increasing to 4 and 5 tools, the percentage
dropped to almost a 60%. The detection failures were because, when the height of the stack of objects
increases, they get too close to the Kinect’s camera, causing them to take up a lot of space in the image and
the detection box fails to recognize them due to their dimensions, while the clamping faults were due to
the fact that, when calculating the height of the element, the heights of the lower tools of the stack are detected,
causing the average to be altered and less than the desired tool. On the other hand, the reinforcement detection
proved to generate a 15.5% improvement in the detection quality of the program, reaching a 90% detection of
elements with a single execution of Step 5, which demonstrates the algorithm's ability to recognize new
elements after the first delivery of tools, and the ability to deliver them almost entirely.
Table 5. Quality of the algorithm applied to a physical environment
Stacked Tools 1 2 3 4 5
1 to 3
stacked objects
4 to 5
stacked objects
General
average
Detection Quality (%) 71.43 100 78.57 64.29 60.31 83.33 62.3 74.92
Holding Quality (%) 100 79.17 85.19 88.89 77.78 88.12 83.34 86.21
Reinforcement Detection (%) 90.48
4. CONCLUSION
The algorithm of detection, classification and tool grip proposed, successfully delivered 80% of
the virtual tools, and 90% of tools in the physical environment, identifying the presence of occlusions and
classifying up to 5 different types of elements. These results demonstrate the ability of the algorithm to hold
and deliver the tools desired by the user overcoming difficulties such as occlusions of one and more
elements. Step 5 allowed to improve the ability to recognize elements in the physical environment by more
than 10% with a single execution, however, it did not generate any effect on the simulated environment,
because the input image in Step 5 does not show large variations with respect to that of Step 2, which avoids
a new detection of tools. The calculation of the height of each tool with respect to the table was affected by
the presence of other elements in the detection box, resulting in a holding quality of 86%. Finally,
a comparison between two detection methods was achieved, such as the Haar classifiers and the fast R-CNN,
where the second presented a 20% improvement in the detection accuracy with respect to the first, in addition
to demonstrating a better ability to recognize elements and classify them correctly as Free or Occlusion.
ACKNOWLEDGEMENTS
The authors are grateful to the Universidad Militar Nueva Granada, which through it’s Vice rectory
for researchs finances the present project with code IMP-ING-2290.
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Algorithm of detection, classification and gripping of occluded objects by CNN techniques... (Paula Useche)
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BIOGRAPHIES OF AUTHORS
Paula Useche Murillo is a Mechatronics Engineer graduated with honors in 2017 from
the Nueva Granada Military University in Bogotá, Colombia, where she currently studies
an M.Sc. in Mechatronics Engineering and works as a research assistant in the Mechatronics
Engineering program.
Robinson Jiménez-Moreno is an electronic engineer graduated from the Francisco José de
Caldas District University in 2002. He received an M.Sc. in Engineering from the National
University of Colombia in 2012 and Ph.D in Engineering at the Francisco José de Caldas District
University in 2018. His current research focuses on the use of convolutional neural networks for
object recognition and image processing for robotic applications such as human-machine
interaction.
Javier Eduardo Martinez Baquero is an Electronic Engineer graduated from University of
the Llanos in 2002. Posgraduated in Electronic Instrumentation from Santo Tomas University in
2004 and M.Sc. in Educative Technology at Autonoma of Bucaramanga University in 2013.
His current research focuses on Instrumentation, Automation and Control.