A major issue for fabric quality inspection is in the detection of defaults, it has become an extremely challenging goal for the textile industry to minimize costs in both production and quality inspection. The quality inspection is currently done manually by professionals; hence the need for the implementation of a fast, powerful, robust, and intelligent machine vision system in order to achieve high global quality, uniformity, and consistency of fabrics and to increase productivity. Consequently, the automatic inspection control process can improve productivity and enhance product quality. This article describes the approach used in developing a convolutional neural network for identifying fabric defects from input images of fabric surfaces. The proposed neural network is a pre-trained convolutional model ‘DetectNet’, it was adapted to be more efficient to the fabric image feature extraction. The developed model is capable of successfully distinguishing between defective fabric and non-defective with 93% accuracy for the first model and 96% for the second model.
FABRIC DEFECT DETECTION BASED ON IMPROVED FASTER RCNNijaia
In the production process of fabric, defect detection plays an important role in the control of product
quality. Consider that traditional manual fabric defect detection method are time-consuming and
inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the
manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention
module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can
infer the attention map from the intermediate feature map and multiply the attention map to adaptively
refine the feature. This method improve the performance of classification and detection without increase
the computation-consuming. The experiment results show that Faster RCNN with attention module can
efficient improve the classification accuracy.
A DEEP LEARNING APPROACH FOR DEFECT DETECTION AND SEGMENTATION IN X-RAY COMPU...gerogepatton
Additive manufacturing is an emerging and crucial technology that can overcome the limitations of
traditional manufacturing techniques to accurately manufacture highly complex parts. X-ray Computed
Tomography (XCT) is a widely used method for non-destructive testing of AM parts. However, detection
and segmentation of defects in XCT images of AM have many challenges due to contrast, size, and
appearance of defects. This study developed deep learning techniques for detecting and segmenting defects
in XCT images of AM. Due to a large number of required defect annotations, this paper applied image
processing techniques to automate the defect labeling process. A single-stage object detection algorithm
(YOLOv5) was applied to the problem of defect detection in image data. Three different variants of
YOLOv5 were implemented and their performances were compared. U-Net was applied for defect
segmentation in XCT slices. Finally, this research demonstrates that deep learning techniques can improve
the automatic defect detection and segmentation in XCT data of AM
Slum image detection and localization using transfer learning: a case study ...IJECEIAES
The document summarizes a study that used transfer learning with seven pretrained convolutional neural network models to detect and localize slums in satellite imagery of northern Morocco. The models - MobileNets, InceptionV3, NASNetMobile, Xception, VGG16, EfficientNet, and ResNet50 - were tested on a dataset of medium-resolution satellite images. The results showed that the top three models for accuracy were MobileNets at 98.78%, InceptionV3 at 97.9%, and NASNetMobile at 97.56%. MobileNets also had the smallest model size and shortest latency, making it well-suited for this task.
Framework for Contextual Outlier Identification using Multivariate Analysis a...IJECEIAES
Majority of the existing commercial application for video surveillance system only captures the event frames where the accuracy level of captures is too poor. We reviewed the existing system to find that at present there is no such research technique that offers contextual-based scene identification of outliers. Therefore, we presented a framework that uses unsupervised learning approach to perform precise identification of outliers for a given video frames concerning the contextual information of the scene. The proposed system uses matrix decomposition method using multivariate analysis to maintain an equilibrium better faster response time and higher accuracy of the abnormal event/object detection as an outlier. Using an analytical methodology, the proposed system blocking operation followed by sparsity to perform detection. The study outcome shows that proposed system offers an increasing level of accuracy in contrast to the existing system with faster response time.
This document presents a new method for detecting anomalies in streaming multivariate time series data using an adapted evolving Spiking Neural Network. The key contributions of the new method are: 1) A rank-order-based learning algorithm that uses spike timing for adjusting synaptic weights, 2) An encoding technique for multivariate data based on multi-dimensional Gaussian Receptive Fields, and 3) A continuous outlier scoring function for improved classification interpretability. The method is shown to outperform other streaming anomaly detection algorithms on a synthetic benchmark dataset, requiring less computational resources for high-dimensional data processing.
Intelligent System For Face Mask DetectionIRJET Journal
This document presents research on developing an intelligent system to detect whether people are wearing face masks or not using deep learning techniques. The system uses a convolutional neural network called MobileNetV2 trained on a dataset of 480 masked and unmasked face images. Data augmentation is used to increase the size of the dataset. OpenCV is used for face detection. The system achieves 99% accuracy on the test dataset and can classify images and video frames in real-time. Applications discussed include use in airports, hospitals, offices and by law enforcement to monitor compliance with mask mandates and prevent the spread of COVID-19.
Social distance and face mask detector system exploiting transfer learningIJECEIAES
As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.
Deep Learning-Based Skin Lesion Detection and Classification: A ReviewIRJET Journal
This document reviews various deep learning techniques for skin lesion detection and classification. It summarizes 10 research papers that detect and segment skin lesions using techniques like convolutional neural networks, fuzzy logic, grabcut algorithm, and adaptive neuro-fuzzy classifiers. The papers achieved good results but had limitations like increased computational time and need for improved segmentation methods. Overall, deep learning has proven effective for skin lesion analysis, but further research is still needed to develop more optimized models.
FABRIC DEFECT DETECTION BASED ON IMPROVED FASTER RCNNijaia
In the production process of fabric, defect detection plays an important role in the control of product
quality. Consider that traditional manual fabric defect detection method are time-consuming and
inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the
manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention
module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can
infer the attention map from the intermediate feature map and multiply the attention map to adaptively
refine the feature. This method improve the performance of classification and detection without increase
the computation-consuming. The experiment results show that Faster RCNN with attention module can
efficient improve the classification accuracy.
A DEEP LEARNING APPROACH FOR DEFECT DETECTION AND SEGMENTATION IN X-RAY COMPU...gerogepatton
Additive manufacturing is an emerging and crucial technology that can overcome the limitations of
traditional manufacturing techniques to accurately manufacture highly complex parts. X-ray Computed
Tomography (XCT) is a widely used method for non-destructive testing of AM parts. However, detection
and segmentation of defects in XCT images of AM have many challenges due to contrast, size, and
appearance of defects. This study developed deep learning techniques for detecting and segmenting defects
in XCT images of AM. Due to a large number of required defect annotations, this paper applied image
processing techniques to automate the defect labeling process. A single-stage object detection algorithm
(YOLOv5) was applied to the problem of defect detection in image data. Three different variants of
YOLOv5 were implemented and their performances were compared. U-Net was applied for defect
segmentation in XCT slices. Finally, this research demonstrates that deep learning techniques can improve
the automatic defect detection and segmentation in XCT data of AM
Slum image detection and localization using transfer learning: a case study ...IJECEIAES
The document summarizes a study that used transfer learning with seven pretrained convolutional neural network models to detect and localize slums in satellite imagery of northern Morocco. The models - MobileNets, InceptionV3, NASNetMobile, Xception, VGG16, EfficientNet, and ResNet50 - were tested on a dataset of medium-resolution satellite images. The results showed that the top three models for accuracy were MobileNets at 98.78%, InceptionV3 at 97.9%, and NASNetMobile at 97.56%. MobileNets also had the smallest model size and shortest latency, making it well-suited for this task.
Framework for Contextual Outlier Identification using Multivariate Analysis a...IJECEIAES
Majority of the existing commercial application for video surveillance system only captures the event frames where the accuracy level of captures is too poor. We reviewed the existing system to find that at present there is no such research technique that offers contextual-based scene identification of outliers. Therefore, we presented a framework that uses unsupervised learning approach to perform precise identification of outliers for a given video frames concerning the contextual information of the scene. The proposed system uses matrix decomposition method using multivariate analysis to maintain an equilibrium better faster response time and higher accuracy of the abnormal event/object detection as an outlier. Using an analytical methodology, the proposed system blocking operation followed by sparsity to perform detection. The study outcome shows that proposed system offers an increasing level of accuracy in contrast to the existing system with faster response time.
This document presents a new method for detecting anomalies in streaming multivariate time series data using an adapted evolving Spiking Neural Network. The key contributions of the new method are: 1) A rank-order-based learning algorithm that uses spike timing for adjusting synaptic weights, 2) An encoding technique for multivariate data based on multi-dimensional Gaussian Receptive Fields, and 3) A continuous outlier scoring function for improved classification interpretability. The method is shown to outperform other streaming anomaly detection algorithms on a synthetic benchmark dataset, requiring less computational resources for high-dimensional data processing.
Intelligent System For Face Mask DetectionIRJET Journal
This document presents research on developing an intelligent system to detect whether people are wearing face masks or not using deep learning techniques. The system uses a convolutional neural network called MobileNetV2 trained on a dataset of 480 masked and unmasked face images. Data augmentation is used to increase the size of the dataset. OpenCV is used for face detection. The system achieves 99% accuracy on the test dataset and can classify images and video frames in real-time. Applications discussed include use in airports, hospitals, offices and by law enforcement to monitor compliance with mask mandates and prevent the spread of COVID-19.
Social distance and face mask detector system exploiting transfer learningIJECEIAES
As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.
Deep Learning-Based Skin Lesion Detection and Classification: A ReviewIRJET Journal
This document reviews various deep learning techniques for skin lesion detection and classification. It summarizes 10 research papers that detect and segment skin lesions using techniques like convolutional neural networks, fuzzy logic, grabcut algorithm, and adaptive neuro-fuzzy classifiers. The papers achieved good results but had limitations like increased computational time and need for improved segmentation methods. Overall, deep learning has proven effective for skin lesion analysis, but further research is still needed to develop more optimized models.
An Innovative Deep Learning Framework Integrating Transfer- Learning And Extr...IRJET Journal
This paper proposes a deep learning framework that uses transfer learning and an XGBoost classifier to classify breast ultrasound images. It uses a VGG16 model pre-trained on general images to extract features from ultrasound images. These features are then classified using an XGBoost classifier. On a dataset of breast ultrasound images, the approach achieved 96.7% accuracy, and precision/recall/F-scores of 100%/96%/96% for benign images, 95%/97%/96% for malignant images, and 95%/98%/97% for normal images, outperforming other automatic image classification methods.
This document discusses using artificial neural networks and MATLAB 7.10 to develop an efficient system for sorting mechanical spare parts. It involves using wavelet transforms to extract features from images of parts, which are then used to train an artificial neural network. The neural network can accurately recognize parts based on their wavelet features with high efficiency. Simulation results show the system can successfully identify the name of a selected spare part from its image with a graphical output.
Artificial Neural Network (ANN) is a fast-growing method which has been used in different
industries during recent years. The main idea for creating ANN which is a subset of artificial
intelligence is to provide a simple model of human brain in order to solve complex scientific and
industrial problems. ANNs are high-value and low-cost tools in modelling, simulation, control,
condition monitoring, sensor validation and fault diagnosis of different systems. It have high
flexibility and robustness in modeling, simulating and diagnosing the behavior of rotating machines
even in the presence of inaccurate input data. They can provide high computational speed for
complicated tasks that require rapid response such as real-time processing of several simultaneous
signals. ANNs can also be used to improve efficiency and productivity of energy in rotating
equipment
End-to-end deep auto-encoder for segmenting a moving object with limited tra...IJECEIAES
The document proposes two end-to-end deep auto-encoder approaches for segmenting moving objects from surveillance videos when limited training data is available. The first approach uses transfer learning with a pre-trained VGG-16 model as the encoder and its transposed architecture as the decoder. The second approach uses a multi-depth auto-encoder with convolutional and upsampling layers. Both approaches apply data augmentation techniques like PCA and traditional methods to increase the training data size. The models are trained and evaluated on the CDnet2014 dataset, achieving better performance than other models trained with limited data.
Development of a Location Invariant Crack Detection and Localisation Model (L...CSCJournals
The document presents a model called LICDAL (Location Invariant Crack Detection and Localization) that was developed to detect and localize cracks in unconstrained oil pipeline images using deep convolutional neural networks. LICDAL applies transfer learning on Faster R-CNN to make the model location invariant by training it on images of cracked pipelines from various locations. When tested on 10 new images from different locations, LICDAL detected and localized cracks with a mean average precision of 97.3%, demonstrating high performance and location invariance. The model performance is compared to an existing crack detection model, showing LICDAL improves crack identification through localization.
Covid Face Mask Detection Using Neural NetworksIRJET Journal
The document describes a study that developed a convolutional neural network (CNN) model using the MobileNetV2 architecture to detect if people in images are wearing face masks properly, improperly, or not at all. The model was trained on a dataset containing these three classes and achieved an accuracy of 97.25% for classifying images. The developed model can be implemented in real-world applications like public transportation stations, hospitals, offices, and schools to help monitor mask compliance and reduce the spread of COVID-19.
Noisy image enhancements using deep learning techniquesIJECEIAES
This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object recognition in noisy images. Compared to single-tasking models, the multi-tasking model showed the superiority of this approach in performing multiple tasks simultaneously and saving training time. This study confirms the effectiveness of using multitasking models using deep learning for object recognition in noisy images. The results obtained can be applied in various fields, including computer vision, robotics, automatic driving, and others, where accurate object recognition in noisy images is a critical component.
Dilated Inception U-Net for Nuclei Segmentation in Multi-Organ Histology ImagesIRJET Journal
The document summarizes a study that used a Dilated Inception U-Net model for nuclei segmentation in histology images. Key points:
1. A Dilated Inception U-Net model was used to segment nuclei in histology images, which employs dilated convolutions to efficiently generate feature maps over a large input area.
2. The model was tested on the MoNuSeg dataset containing H&E stained images. Preprocessing included color normalization, data augmentation, and extracting 256x256 patches.
3. The Dilated Inception U-Net modifies the classic U-Net by replacing convolutional blocks with dilated inception blocks containing 1x1 and 3x3 filters with different dilation rates, allowing it to
This document describes research on using mobile hyperspectral imaging to detect material surface damage. It discusses how hyperspectral imaging captures hundreds of spectral reflectance values per pixel, providing more information than traditional RGB images. The researchers developed a mobile hyperspectral imaging system and machine learning models to classify different surface objects, including cracks. Experimental results showed hyperspectral pixels could identify eight surface objects with high accuracy, outperforming gray-valued images with higher spatial resolution. The researchers conclude hyperspectral imaging has great potential for automating damage inspection, especially for complex scenes on built structures.
Acoustic event characterization for service robot using convolutional networksIJECEIAES
This paper presents and discusses the creation of a sound event classification model using deep learning. In the design of service robots, it is necessary to include routines that improve the response of both the robot and the human being throughout the interaction. These types of tasks are critical when the robot is taking care of children, the elderly, or people in vulnerable situations. Certain dangerous situations are difficult to identify and assess by an autonomous system, and yet, the life of the users may depend on these robots. Acoustic signals correspond to events that can be detected at a great distance, are usually present in risky situations, and can be continuously sensed without incurring privacy risks. For the creation of the model, a customized database is structured with seven categories that allow to categorize a problem, and eventually allow the robot to provide the necessary help. These audio signals are processed to produce graphical representations consistent with human acoustic identification. These images are then used to train three convolutional models identified as high-performing in this type of problem. The three models are evaluated with specific metrics to identify the best-performing model. Finally, the results of this evaluation are discussed and analyzed.
Efficient lane marking detection using deep learning technique with differen...IJECEIAES
Nowadays, researchers are incorporating many modern and significant features on advanced driver assistance systems (ADAS). Lane marking detection is one of them, which allows the vehicle to maintain the perspective road lane. Conventionally, it is detected through handcrafted and very specialized features and goes through substantial post-processing, which leads to high computation, and less accuracy. Additionally, this conventional method is vulnerable to environmental conditions, making it an unreliable model. Consequently, this research work presents a deep learningbased model that is suitable for diverse environmental conditions, including multiple lanes, different daytime, different traffic conditions, good and medium weather conditions, and so forth. This approach has been derived from plain encode-decode E-Net architecture and has been trained by using the differential and cross-entropy losses for the backpropagation. The model has been trained and tested using 3,600 training and 2,700 testing images from TuSimple, a robust public dataset. Input images from very diverse environmental conditions have ensured better generalization of the model. This framework has reached a max accuracy of 96.61%, with an F1 score of 96.34%, a precision value of 98.91%, and a recall of 93.89%. Besides, this model has shown very small false positive and false negative values of 3.125% and 1.259%, which bits the performance of most of the existing state of art models.
Image Forgery Detection Methods- A ReviewIRJET Journal
This document reviews various methods for detecting image forgery. It begins with an introduction to the topic, explaining the need for image forgery detection techniques due to the widespread manipulation of images online. It then categorizes common types of image manipulation and provides a literature review comparing the accuracy and citations of different detection techniques, such as CNN-based methods, transform-domain methods using DCT and DWT, and methods analyzing JPEG compression artifacts. The review finds that CNN-based methods generally achieve the highest accuracy, around 90-100%, but also notes transform-domain and JPEG-based methods can also achieve reasonably high accuracy ranging from 70-100% depending on the technique and testing parameters.
The document presents a novel multi-view multi-level network (MMNet) for fault diagnosis that accommodates feature transferability. Existing deep transfer learning solutions for fault diagnosis focus on domain adaptation by minimizing data distribution discrepancies, but neglect unique domain-specific features. MMNet constructs two network channels - one for learning common cross-domain features using multi-kernel maximum mean discrepancy, and another for learning domain-specific features using combined domain and fault classification. MMNet also adopts a few-shot learning approach using two modules for feature extraction and relation computation, enabling zero-shot fault classification in the target domain. Experimental results demonstrate MMNet achieves state-of-the-art performance on transfer tasks for fault diagnosis.
Deep Learning based Multi-class Brain Tumor ClassificationIRJET Journal
The document discusses a study that aims to improve the classification of brain tumors on MRI images using deep learning techniques. It compares several convolutional neural network architectures (Custom CNN, DenseNet169, MobileNet, VGG-16, and ResNet152) for multi-class brain tumor classification using MRI data. The models are trained on a dataset of approximately 5,000 brain MR images and their performance at tumor detection is evaluated and compared. Transfer learning techniques are also discussed for applying knowledge from one task to improve predictions for new tasks.
Accuracy study of image classification for reverse vending machine waste segr...IJECEIAES
This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1- score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.
A Literature Survey on Image Linguistic Visual Question AnsweringIRJET Journal
This document discusses a literature survey on image and linguistic visual question answering. It aims to develop a model that achieves higher performance than state-of-the-art solutions by exploring different existing models and developing a custom model. The paper reviews several existing models for visual question answering and image classification using convolutional neural networks. It also discusses developing a new dataset for visual question answering using automated question generation from image descriptions.
A simplified and novel technique to retrieve color images from hand-drawn sk...IJECEIAES
This document presents a novel technique for retrieving color images from hand-drawn sketches. The proposed method uses K-means clustering and bag-of-attributes approaches to extract key information from sketches. It introduces a unique indexing scheme to make the retrieval process faster and more accurate. The study implemented the technique in MATLAB and found it offered better accuracy and faster processing times than existing feature extraction methods.
Applying convolutional neural networks for limited-memory applicationTELKOMNIKA JOURNAL
Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...gerogepatton
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
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.
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Covid Face Mask Detection Using Neural NetworksIRJET Journal
The document describes a study that developed a convolutional neural network (CNN) model using the MobileNetV2 architecture to detect if people in images are wearing face masks properly, improperly, or not at all. The model was trained on a dataset containing these three classes and achieved an accuracy of 97.25% for classifying images. The developed model can be implemented in real-world applications like public transportation stations, hospitals, offices, and schools to help monitor mask compliance and reduce the spread of COVID-19.
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This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object recognition in noisy images. Compared to single-tasking models, the multi-tasking model showed the superiority of this approach in performing multiple tasks simultaneously and saving training time. This study confirms the effectiveness of using multitasking models using deep learning for object recognition in noisy images. The results obtained can be applied in various fields, including computer vision, robotics, automatic driving, and others, where accurate object recognition in noisy images is a critical component.
Dilated Inception U-Net for Nuclei Segmentation in Multi-Organ Histology ImagesIRJET Journal
The document summarizes a study that used a Dilated Inception U-Net model for nuclei segmentation in histology images. Key points:
1. A Dilated Inception U-Net model was used to segment nuclei in histology images, which employs dilated convolutions to efficiently generate feature maps over a large input area.
2. The model was tested on the MoNuSeg dataset containing H&E stained images. Preprocessing included color normalization, data augmentation, and extracting 256x256 patches.
3. The Dilated Inception U-Net modifies the classic U-Net by replacing convolutional blocks with dilated inception blocks containing 1x1 and 3x3 filters with different dilation rates, allowing it to
This document describes research on using mobile hyperspectral imaging to detect material surface damage. It discusses how hyperspectral imaging captures hundreds of spectral reflectance values per pixel, providing more information than traditional RGB images. The researchers developed a mobile hyperspectral imaging system and machine learning models to classify different surface objects, including cracks. Experimental results showed hyperspectral pixels could identify eight surface objects with high accuracy, outperforming gray-valued images with higher spatial resolution. The researchers conclude hyperspectral imaging has great potential for automating damage inspection, especially for complex scenes on built structures.
Acoustic event characterization for service robot using convolutional networksIJECEIAES
This paper presents and discusses the creation of a sound event classification model using deep learning. In the design of service robots, it is necessary to include routines that improve the response of both the robot and the human being throughout the interaction. These types of tasks are critical when the robot is taking care of children, the elderly, or people in vulnerable situations. Certain dangerous situations are difficult to identify and assess by an autonomous system, and yet, the life of the users may depend on these robots. Acoustic signals correspond to events that can be detected at a great distance, are usually present in risky situations, and can be continuously sensed without incurring privacy risks. For the creation of the model, a customized database is structured with seven categories that allow to categorize a problem, and eventually allow the robot to provide the necessary help. These audio signals are processed to produce graphical representations consistent with human acoustic identification. These images are then used to train three convolutional models identified as high-performing in this type of problem. The three models are evaluated with specific metrics to identify the best-performing model. Finally, the results of this evaluation are discussed and analyzed.
Efficient lane marking detection using deep learning technique with differen...IJECEIAES
Nowadays, researchers are incorporating many modern and significant features on advanced driver assistance systems (ADAS). Lane marking detection is one of them, which allows the vehicle to maintain the perspective road lane. Conventionally, it is detected through handcrafted and very specialized features and goes through substantial post-processing, which leads to high computation, and less accuracy. Additionally, this conventional method is vulnerable to environmental conditions, making it an unreliable model. Consequently, this research work presents a deep learningbased model that is suitable for diverse environmental conditions, including multiple lanes, different daytime, different traffic conditions, good and medium weather conditions, and so forth. This approach has been derived from plain encode-decode E-Net architecture and has been trained by using the differential and cross-entropy losses for the backpropagation. The model has been trained and tested using 3,600 training and 2,700 testing images from TuSimple, a robust public dataset. Input images from very diverse environmental conditions have ensured better generalization of the model. This framework has reached a max accuracy of 96.61%, with an F1 score of 96.34%, a precision value of 98.91%, and a recall of 93.89%. Besides, this model has shown very small false positive and false negative values of 3.125% and 1.259%, which bits the performance of most of the existing state of art models.
Image Forgery Detection Methods- A ReviewIRJET Journal
This document reviews various methods for detecting image forgery. It begins with an introduction to the topic, explaining the need for image forgery detection techniques due to the widespread manipulation of images online. It then categorizes common types of image manipulation and provides a literature review comparing the accuracy and citations of different detection techniques, such as CNN-based methods, transform-domain methods using DCT and DWT, and methods analyzing JPEG compression artifacts. The review finds that CNN-based methods generally achieve the highest accuracy, around 90-100%, but also notes transform-domain and JPEG-based methods can also achieve reasonably high accuracy ranging from 70-100% depending on the technique and testing parameters.
The document presents a novel multi-view multi-level network (MMNet) for fault diagnosis that accommodates feature transferability. Existing deep transfer learning solutions for fault diagnosis focus on domain adaptation by minimizing data distribution discrepancies, but neglect unique domain-specific features. MMNet constructs two network channels - one for learning common cross-domain features using multi-kernel maximum mean discrepancy, and another for learning domain-specific features using combined domain and fault classification. MMNet also adopts a few-shot learning approach using two modules for feature extraction and relation computation, enabling zero-shot fault classification in the target domain. Experimental results demonstrate MMNet achieves state-of-the-art performance on transfer tasks for fault diagnosis.
Deep Learning based Multi-class Brain Tumor ClassificationIRJET Journal
The document discusses a study that aims to improve the classification of brain tumors on MRI images using deep learning techniques. It compares several convolutional neural network architectures (Custom CNN, DenseNet169, MobileNet, VGG-16, and ResNet152) for multi-class brain tumor classification using MRI data. The models are trained on a dataset of approximately 5,000 brain MR images and their performance at tumor detection is evaluated and compared. Transfer learning techniques are also discussed for applying knowledge from one task to improve predictions for new tasks.
Accuracy study of image classification for reverse vending machine waste segr...IJECEIAES
This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1- score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.
A Literature Survey on Image Linguistic Visual Question AnsweringIRJET Journal
This document discusses a literature survey on image and linguistic visual question answering. It aims to develop a model that achieves higher performance than state-of-the-art solutions by exploring different existing models and developing a custom model. The paper reviews several existing models for visual question answering and image classification using convolutional neural networks. It also discusses developing a new dataset for visual question answering using automated question generation from image descriptions.
A simplified and novel technique to retrieve color images from hand-drawn sk...IJECEIAES
This document presents a novel technique for retrieving color images from hand-drawn sketches. The proposed method uses K-means clustering and bag-of-attributes approaches to extract key information from sketches. It introduces a unique indexing scheme to make the retrieval process faster and more accurate. The study implemented the technique in MATLAB and found it offered better accuracy and faster processing times than existing feature extraction methods.
Applying convolutional neural networks for limited-memory applicationTELKOMNIKA JOURNAL
Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...gerogepatton
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
Similar to Automatic fabric defect detection employing deep learning (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%.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
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Automatic fabric defect detection employing deep learning
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 4, August 2022, pp. 4129~4136
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i4.pp4129-4136 4129
Journal homepage: http://ijece.iaescore.com
Automatic fabric defect detection employing deep learning
Aafaf Beljadid1
, Adil Tannouche2
, Abdessamad Balouki1
1
Engineering and Applied Physics Team (EAPT), ESTBM, University Sultan Moulay Slimane, Beni Mellal, Morocco
2
Laboratory of Engineering and Applied Technology (LITA), ESTBM, University Sultan Moulay Slimane, Beni Mellal, Morocco
Article Info ABSTRACT
Article history:
Received Sep 13, 2021
Revised Feb 16, 2022
Accepted Mar 13, 2022
A major issue for fabric quality inspection is in the detection of defaults, it
has become an extremely challenging goal for the textile industry to
minimize costs in both production and quality inspection. The quality
inspection is currently done manually by professionals; hence the need for
the implementation of a fast, powerful, robust, and intelligent machine
vision system in order to achieve high global quality, uniformity, and
consistency of fabrics and to increase productivity. Consequently, the
automatic inspection control process can improve productivity and enhance
product quality. This article describes the approach used in developing a
convolutional neural network for identifying fabric defects from input
images of fabric surfaces. The proposed neural network is a pre-trained
convolutional model ‘DetectNet’, it was adapted to be more efficient to the
fabric image feature extraction. The developed model is capable of
successfully distinguishing between defective fabric and non-defective with
93% accuracy for the first model and 96% for the second model.
Keywords:
Deep learning
DetectNet
Fabric defect
Object detection
Quality control
This is an open access article under the CC BY-SA license.
Corresponding Author:
Aafaf Beljadid
Engineering and Applied Physics Team (EAPT), ESTBM, Sultane Moulay Slimane University
Avenue Mohamed V, BP 591, Beni Mellal, Morocco
Email: Aafaf.beljadid@usms.ac.ma
1. INTRODUCTION
With the fast progress of computer science and image processing technologies, computer vision
technology has been applied largely in the textile industry. Consequently, the automatic inspection of fabric
defect will become a self-evident solution to achieve high quality and lowest manpower prices [1]. Deep
learning and computer vision have been largely used in textile fields, and numerous researches are available
on this subject [2]–[7].
Deep learning is a leading machine learning technique which use artificial neural networks; it tends
to be able to handle higher levels of abstracting data by using horizontal hierarchical architectures [8]. This
approach is recent and has been largely used in many areas of artificial intelligence, such as health care,
automotive industry, natural language processing, document analysis and recognition [9]–[12]. Deep neuron
networks (DNNs) for image classification generally combine layers of convolutional neural networks
(CNNs) and fully connected artificial neurons stacked to satisfy overlaying vision areas.
CNN is a category of deep learning and it is considered to be the leading classification system for
image identification and classification problems. Instead of standard algorithms, CNNs are able to acquire
advanced characteristics from the initial image without having to extract the specific features manually [13].
It has demonstrated an exceptional capacity in image treatment and categorization [2], [13]–[16].
The identification of fabric defects is of great interest to industrial and scientific researchers. The
objective of identifying diverse anomalous designs in a complicated setting. Several approaches to identify
defects under various hypotheses are available [17]–[22]. Abouelela et al. [20] supposed that the fabric
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structure is a composite of many basic shapes and determined that an area with dissimilar shapes was
defective. Because there are relevant and significant dissimilarities between defective and non-defective
structures over frequency spectrum, Chan [21] employed a Fourier transform to distinguish the defective and
non-defective areas.
The deep learning approach has recently made an immense contribution to solving a wide number of
computer vision challenges. Some methods [6], [23]–[26] implemented deep learning to identify defects in
fabrics. On the basis of the trained and learned subdictionaries, Zhao et al. [26] developed a CNN model
based on embedded short- and long-term visual storage for the classification of fabric images. The authors
noted that the results show that deep learning methods conceived to solve a different classification task can
be easily adapted to the problem of fabric fault classification, revealing the need for a well-conceived
architecture. Although these methods have obtained good results in particular cases, most of them are
restricted to the use of simple structures and are unable to deal with the real-world complexity of texture
defect detection. In order to boost the efficiency of detecting fabric defects in the real world, a variety of
important issues are required to be resolved. Firstly, labeling various real-world fabric faults is labor and
required time. Due to the sophisticated variety of fabric and fault components, collecting a dataset of labelled
data that spans all potential fabric textures is complicated and expensive. For example, when it comes to
fabrics with unseen textures or materials, pre-trained detection models typically do not work well.
In this paper, the ability of DetectNet, a CNN, to detect fabric defects in the "Tilda" dataset was
proposed to be evaluated in order to develop a reliable system capable of detecting defects in real time. This
work is structured as such. An overview on the literature of fabric defect detection methods. Next, the
proposed method for automatic defect detection is bounced, including dataset preparation and description of
the proposed network model. The results and discussion are presented, and a conclusion is provided.
2. REVIEW OF PREVIOUS WORK
The majority of available literature on tissue investigation is primarily concerned with uniform
textiles [27], [28], namely plain and woven fabrics. There are four principal classes of these methods that can
be classified into: i) statistics approaches, ii) spectral approaches, iii) model-based approaches, and
iv) learning-based approaches [28]. Among statistics approaches, the autocorrelation function and the
co-occurrence matrix [29], [30] have been effectively used to detect the defects. Zhu et al. [28] used a
combination of autocorrelation function and gray level co-occurrence matrix (GLCM) approaches to detecting
defects in yarn-dyed fabrics. The autocorrelation function is used to specify a scale for the pattern images.
GLCM is able to map image features, like contrast. However, such approaches are very labor-intensive.
Among the largely employed spectral approaches for defect detection are the Fourier transform [21],
the wavelet transform [31], [32] and the Gabor filters [33]. Chan [21] applied Fourier transform to identify
the structure fault of the tissue. To understand the behavior of the frequency spectrum, simulation techniques
are used. A shortcoming when applying Fourier transform is that local data in the spatial region is not
accessible and there is no tolerance for minor faults. As opposed to Fourier transform, Gabor filters and
wavelet transform use spatial frequency analysis, which permits the identification of nearby imperfections.
Ngan et al. [34] utilized the wavelet transform to consequently distinguish defaults on designed texture with
a precision of 96.7%, Serdaroglu et al. [31] introduced a strategy that depends upon wavelet transformation
before the analysis of autonomous features to handle default detection of textile fabric images, and Deotale
and Sarode [33] proposed an algorithm in light of GLCM and Gabor wavelet extraction and refuse derived
fuel (RDF) characterization technic, which describes the structure of fabric and featured the defect position.
Model-based techniques are utilized to tackle the deformity identification issue by expecting that the
surface complies with a specific dissemination model and that the model's boundaries are assessed.
Yapi et al. [35] separated the picture into rudimentary monotonous units and recreated the conveyance of
excess contourlet change (RCT) coefficients utilizing a limited combination of a summed up Gaussian
model. These strategies can manage different kinds of material textures.
Learning-based methodologies are likewise famous in identifying defaults, utilizing labeling tests to
prepare classifiers that recognize default and non-default examples. CNN has strong performance in
detecting, segmenting, analyzing, and reporting information, and has been used in a variety of applications in
the field of environmental information. From the beginning of the 2000s, with the quick progress of big data
and artificial intelligence, convolutional networks have very successfully implemented in data detecting,
segmenting [18], [32], and identification of targets and areas in an image [4], especially in tasks involving a
high volume of labelled data, such as surface finish [36], industry [10], heath images [37]–[39], and weed
detection [16], [40], [41]. In our previous work [2], three famous pre-trained CNN models are compared to
detect defect in fabric texture, and the three models automatically detected imperfections on designed texture
with an exactness of 96%.
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3. MATERIALS AND METHODS
3.1. Object detection
Object-based detection is able to identify single targets in the picture and position bounding boxes
around the target. Successful detection of objects requires a system that can handle either the presence (or
not) of items in arbitrary situations, can be scaled-independent, view of the item is not depending on the
orientation, and can also detect semi-black-out objects. The images of the real environment sometimes
include a couple of objects or a huge variety of objects; this may affect the precision and efficiency of the
object detector.
Transfer learning is currently deployed in deep learning applications to speed up training and
increase the accuracy of the learning system. It means learning different techniques for a pre-trained network
and uses it as a starting point. This can be very quick and easy instead of building a new network. You can
quickly transfer the learned functions to a new task using a reduced number of training images. The use of
pre-trained models accelerates the training and decreases the related fees for collecting massive data sets,
labeling, and training models from zero. Training through transfer learning with pre-trained models are
suitable for artificial intelligence (AI) applications in industrial inspection, health care, e-learning, and many
other areas. Following is a description of the CNN model “DetectNet” employed in this research.
3.2. DetectNet architecture
DetectNet is an item location design made by NVIDIA. It tends to be run from NVIDIA's deep
learning graphical UI, DIGITS, which permits to arrangement and begin preparing characterization, object
recognition, division, and different sorts of quick models. The DetectNet includes two prototype files
delivered by NVIDIA: original single classes file and dual class file. The DetectNet architecture, see in
Figures 1 and 2 is composed of five sections according to the Caffe model description folder [42]: i) a data
layers embed the he pictures and names and a transformer layer is applying the online data augmentation,
ii) a fully connected network (FCN) is used to extract features and predict object classes and boxes delimiters
en grid square, iii) loss functions simultaneously measure the error in the two tasks of predicting the object
coverage and object bounding box corners per grid square, iv) a clustering function produces the final set of
predicted bounding boxes during validation, and v) a simplified version of the mean average precision (mAP)
metric is computed to measure model performance against the validation dataset.
Figure 1. DetectNet training architecture [43]
Figure 2. DetectNet validation architecture [43]
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DetectNet is a continuation of a famous architecture, GoogLeNet; the DetectNet FCN has similar
construction to GoogLeNet except for the input data layer, the final pooling layer, and the output layers [44].
The main advantage of the use of a pre-formed GoogLeNet model to initialize DetectNet is to minimize the
time needed for training and to ensure better accuracy for the model. The images in the training set should
not be of different sizes. Otherwise, pad them or resize them to equal size, the size must divide the stride.
1,248 by 384 pixels, the default image size of DetectNet, it is divided by 16. DetectNet has the ability to
identify boxes that are within the size range of 50×50 pixels to 400×400 pixels, but it is difficult to detect
boundary boxes outside this band. Training processes for object detection were realized through the CNN
DetectNet conducted with the library NVIDIA DIGITS [45] release 5.0 on the Caffe framework.
3.3. Data preparation
Experiments have been conducted on the popular TILDA database, is a textile texture database
which was created inside the structure of the functioning gathering texture analysis of the Deutsche
Forschungsgemeinschafts (DFGs) significant examination program "Programmed Visual Inspection of
Technical Objects". This functioning gathering created and dissected strategies which made it conceivable to
perceive and recognize surfaces of changing sorts [46]. This database consists of eight representative textile
kinds, seven error classes and a non-defect class, which four main groups (C1-C4) with each group consisting
of two different subgroups, see Figure 3. Therefore, each sub-folder holds just a single fabric kind of image,
each of them being divided into 8 sub-folders containing 50 texture images in total. The first sub-folder
labeled "e0" includes non-defect images, while the rest of the sub-folders ("e1"-"e7") include defect images.
All images are resized to meet the architecture input size. In the studied cases, NVIDIA’s DetectNet
will be used as the main object detection model in DIGITS v5 [47]. IrfanView Software is used to resize
images to be of equal dimensions width 1024 and height 512. Labeling images for object detection is a process
where we create files that contain descriptions about regions of interest on images (ROI), as shown in Figure 4.
ROI consists of a quadratic box or box delimited by a zone in a picture that contains the object to be detected.
There are a few formats for labeling object detection data, but NVIDIA’s DetectNet uses the KITTI format.
Fiji software with the ALP's plugin was used to label all images, showing the position of the defect in the
picture. The defective areas were tagged by defining rectangular ROIs of varying size to surround the
defaults as shown in Figure 5. The upper left (x1, y1) and lower right (x2, y2) corner coordinates of the ROIs
were identified and translated into text, see Figure 4. Then, the areas were allocated to two categories based
on defect availability or not, category 0 for areas with defaults and category 1 for areas with no defects.
Images and labels should be split into 3 folders: training, validation and testing. Each of them should
also contain two folders, “images” and “labels”. Validation (Val) folder should contain about 10% of the
images and labels from your original folder, testing (Test) folder should contain 10% and training (Train)
folder should contain the other 80%. By doing this we are giving DIGITS a folder to be trained on, a folder to
be validated on and a folder to be tested on as shown in Figure 6. After every training set using the images
and labels in the "Train" file, DIGITS tries to validate the model through the images and labels in the "Val"
file. The aim is to achieve the most accurate results possible. Finally, the last step is to test the model using
the test folder. The "Testing" dataset was created in order to evaluate the model efficiency on an entirely
fresh dataset.
A small dataset of 1,000 images were only used, which was distributed arbitrarily to training,
validation, and testing datasets. 800 of the images were used for training, 100 for validation and 100 for
testing. The validation and training dataset consisted of 50% non-defective (Class 0) and 50% defective
(Class 1). To improve the learning capacity when dealing with small data sets, data augmentation is used to
increase the size of data sets by modifying and adapting image focus, luminosity, and sharpness through
image treatment technology (IrfanView version 4.54). As a result, the training dataset were increased to
12,000 patches. Two learning models were performed using the same datasets for training (training and
validation) and testing, DetectNet has been trained over 300 epochs.
Figure 3. Examples of defective fabric images from TILDA database
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Figure 4. Corner coordinates: upper left (x1, y1) and lower right (x2, y2)
Figure 5. A fabric image showing ROI (region of interest) designated by the blank box, with the corners
coordinates
Figure 6. Input folder structure for images and labels
4. RESULTS AND DISCUSSION
The DIGITS implementation survey depends on multiple measures. Nevertheless, in this paper, just
four metrics are considered: accuracy, sensitivity, specificity, and the area under the receiver operating
characteristic curve (AUC). The measures are best represented as follow [22]–[24]:
− Accuracy: This is the degree of prediction efficiency; it measures the number of correct predictions.
− Sensitivity: This is the number of correct positive decisions divided by the number of true positives.
− Specificity: The number of true negative decisions divided by the number of actually negative cases.
− The false positive fraction (FPF) = 1-specificity.
− AUC: Is the area under the receiver operating characteristic curve, as shown in Figure 7.
− ROC: Receiver operating characteristic curves, which are defined as a plot of F (1-specificity, sensitivity).
After training the model for NVIDIA dataset in KITTI format, the performance of the model is as
shown in Table 1. A significant difference were found in the Area under the receiver operating characteristic
curve AUCs between the first and the second models. The sensitivity for detection of the default (class 0)
was 0.90 for the first model and 0.92 for the second model. The sensitivity is higher than 0.90 which
indicates in 90% of the images the learning machine can properly detect the existence of default. The
investigation concluded that the use of DIGITS and DetectNet has achieved higher sensitivity and
classification values in textile default detection.
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The second model has arrived at 96% accuracy as shown in Table 1, which means that the model
achieves high precision. Moreover, the sensitivity reaches 94% means that the model is efficient in
identifying the relevant data as shown in Figure 7. Therefore, these experiments have demonstrated good
achievement in training the CNN model to detect defect in textile.
Deep learning conquered various different fields namely: health [38], [48], textile [2], [5], weed
detection [16], [49] and many others [50], [51]. But not many studies have outlined the implementation of
deep learning in textile quality control. The essence of the object detection system is to identify the location
of an object in a specific image and to classify them. The database that is used consists of a restricted
database of fabric images, to improve the model more fabric images are needed for training the model that
will lead to improving it. Currently, hard working in collaboration with multiple textile firms is being done to
create a large dataset of more than 100,000 labeled fabric images to boost the performance of the recognition
system in data acquisition.
Figure 7. The ROC curves for the first and second models
Table 1. Performances of the deep learning systems "DetectNet" for detecting fabric defects
Variable First Model Second Model
Accuracy 0.93 0.96
Sensitivity 0.91 0.94
Specificity 0.96 1.00
AUC 0.93 0.96
5. CONCLUSION
In this paper, a pre-trained convolution neural network “DetectNet” has been fine-tuned to detect the
presence of defect in fabric images. The objective of this work was to elaborate an automatic fabric
inspection system able to detect fabric defect images. Experimental results show an accuracy of 96% for the
second model. The achieved systems were successful in this research. The neural network systems, for the
identification and classification of fabric defects, have shown an overall precision equal to or greater than
90%. In addition, a system containing these models for identifying and classifying a single failure provides
an automated mechanism that highlights and displays the probability of classifying multiple types of failures
with the accuracy shown in the previous paragraph. However, despite the promising results, there are still
possibilities for improvement which are proposed as a future work, recognizing fabric defect in images with a
multi-label approach is the essential aim of the convey.
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BIOGRAPHIES OF AUTHORS
Aafaf Beljadid received an engineer’s degree in Industrial Engineering from the
High School of Textile and Clothing Industries ESITH in 2015. Currently, she is a Ph.D
student at the Faculty of Sciences and Technology, University of Sultan Moulay Slimane,
Beni Mellal, Morocco. Her area of research includes machine learning, artificial intelligence
and their application in textile. She can be contacted at email: Aafaf.beljadid@gmail.com.
Adil Tannouche is a professor at the Higher School of Technology, Béni Mellal,
Sultan Moulay Slimane University, Morocco. He received his PhD in electronics and
embedded systems from Moulay Ismail University, Mèknes, Morocco. He is member of
Laboratory of Engineering and Applied Technologies and his research area includes machine
vision, artificial intelligence and applications in the field of precision agriculture and agro-
industry. He can be contacted at email: Tannouche@gmail.com.
Abdessamad Balouki is a professor at the department of mechatronics in the
High School of Technology Beni Mellal, University of Sultan Moulay Slimane, Beni Mellal,
Morocco. His area of research includes information and communication technology, computer
networking, mechatronics, electronics and communication engineering, and chemo metrics.
He can be contacted at email: Balouki@gmail.com.