Road markers guide the driver while driving on the road to control the traffic for the safety of
the road users. With the booming autonomous car technology, the road markers classification is important
in its vision segment to navigate the autonomous car. A new method is proposed in this paper to classify
five types of road markers namely dashed, single, double, solid-dashed and dashed-solid which are
commonly found on the two lane single carriageway. The classification is using unique feature acquired
from the binary image by scanning on each of the images to calculate the frequency of binary transition.
Another feature which is the slopes between the two centroids which allow the proposed method, to
perform the classification within the same video frame period. This proposed method has been observed to
achieve an accuracy value of at least 93%, which is higher than the accuracy value achieved by
the existing methods.
Real-Time Video Processing using Contour Numbers and Angles for Non-urban Roa...IJECEIAES
Road users make vital decisions to safely maneuver their vehicles based on the road markers, which need to be correctly classified. The road markers classification is significantly important especially for the autonomous car technology. The current problems of extensive processing time and relatively lower average accuracy when classifying up to five types of road markers are addressed in this paper. Two novel real time video processing methods are proposed by extracting two formulated features namely the contour number, , and angle, 휃 to classify the road markers. Initially, the camera position is calibrated to obtain the best Field of View (FOV) for identifying a customized Region of Interest (ROI). An adaptive smoothing algorithm is performed on the ROI before the contours of the road markers and the corresponding two features are determined. It is observed that the achievable accuracy of the proposed methods at several non-urban road scenarios is approximately 96% and the processing time per frame is significantly reduced when the video resolution increases as compared to that of the existing approach.
An index based road feature extraction from LANDSAT-8 OLI imagesIJECEIAES
Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (Top-hat or Bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (SSF), is proposed. Then, an iterative adaptive threshold (IAT) based online search with variational min-max and markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RI) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper.
This presentation describes a framework of map image analysis and presentation of the semantic information to visually impaired users using alternative modalities (i.e. haptics and audio). The aim of the proposed framework is to provide the visually impaired users with an easy way to use means of accessing conventional 2D maps. The proposed framework utilizes novel algorithms for the segmentation of the map images using morphological filters that are able to provide indexed information on both the street network structure and the positions of the street names in the map. The user can interact with the produced 3D model of the map and examine its properties. The developed framework analyses the map image so as to obtain the enclosed information. While navigating, audio messages are displayed providing information about the current position of the user (e.g. street name).
A Much Advanced and Efficient Lane Detection Algorithm for Intelligent Highwa...cscpconf
This paper presents a much advanced and efficient lane detection algorithm. The algorithm is based on (ROI) Region of Interest segmentation. In this algorithm images are pre-processed by top-hat transform for de-noising and enhancing contrast. ROI of a test image is then extracted. For detecting lines in the ROI, Hough transform is used. Estimation of the distance between Hough origin and lane-line midpoint is made. Lane departure decision is made based on the difference between these distances. As for the simulation part we have used Matlab software.Experiments show that the proposed algorithm can detect the lane markings accurately and quickly.
Improved algorithm for road region segmentation based on sequential monte car...csandit
In recent years, many researchers and car makers put a lot of intensive effort into development
of autonomous driving systems. Since visual information is the main modality used by human
driver, a camera mounted on moving platform is very important kind of sensor, and various
computer vision algorithms to handle vehicle surrounding situation are under intensive
research. Our final goal is to develop a vision based lane detection system with ability to
handle various types of road shapes, working on both structured and unstructured roads, ideally
under presence of shadows. This paper presents a modified road region segmentation algorithm
based on sequential Monte-Carlo estimation. Detailed description of the algorithm is given,
and evaluation results show that the proposed algorithm outperforms the segmentation
algorithm developed as a part of our previous work, as well as an conventional algorithm based
on colour histogram.
IRJET-Road Detection from Satellite ImagesIRJET Journal
This document summarizes a research paper about detecting roads from satellite images. It begins with an abstract stating that the goal is to propose a more accurate model for road detection compared to current methods using Google Maps, which can be outdated or inaccurate. It then provides background on roads in India and issues with existing navigation systems sometimes showing roads that are under construction. The paper proposes using Otsu's method and morphological operations on input images to better detect roads from satellite imagery. It also reviews related work on using neural networks and color features for road detection from aerial/satellite images.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
Real-Time Video Processing using Contour Numbers and Angles for Non-urban Roa...IJECEIAES
Road users make vital decisions to safely maneuver their vehicles based on the road markers, which need to be correctly classified. The road markers classification is significantly important especially for the autonomous car technology. The current problems of extensive processing time and relatively lower average accuracy when classifying up to five types of road markers are addressed in this paper. Two novel real time video processing methods are proposed by extracting two formulated features namely the contour number, , and angle, 휃 to classify the road markers. Initially, the camera position is calibrated to obtain the best Field of View (FOV) for identifying a customized Region of Interest (ROI). An adaptive smoothing algorithm is performed on the ROI before the contours of the road markers and the corresponding two features are determined. It is observed that the achievable accuracy of the proposed methods at several non-urban road scenarios is approximately 96% and the processing time per frame is significantly reduced when the video resolution increases as compared to that of the existing approach.
An index based road feature extraction from LANDSAT-8 OLI imagesIJECEIAES
Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (Top-hat or Bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (SSF), is proposed. Then, an iterative adaptive threshold (IAT) based online search with variational min-max and markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RI) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper.
This presentation describes a framework of map image analysis and presentation of the semantic information to visually impaired users using alternative modalities (i.e. haptics and audio). The aim of the proposed framework is to provide the visually impaired users with an easy way to use means of accessing conventional 2D maps. The proposed framework utilizes novel algorithms for the segmentation of the map images using morphological filters that are able to provide indexed information on both the street network structure and the positions of the street names in the map. The user can interact with the produced 3D model of the map and examine its properties. The developed framework analyses the map image so as to obtain the enclosed information. While navigating, audio messages are displayed providing information about the current position of the user (e.g. street name).
A Much Advanced and Efficient Lane Detection Algorithm for Intelligent Highwa...cscpconf
This paper presents a much advanced and efficient lane detection algorithm. The algorithm is based on (ROI) Region of Interest segmentation. In this algorithm images are pre-processed by top-hat transform for de-noising and enhancing contrast. ROI of a test image is then extracted. For detecting lines in the ROI, Hough transform is used. Estimation of the distance between Hough origin and lane-line midpoint is made. Lane departure decision is made based on the difference between these distances. As for the simulation part we have used Matlab software.Experiments show that the proposed algorithm can detect the lane markings accurately and quickly.
Improved algorithm for road region segmentation based on sequential monte car...csandit
In recent years, many researchers and car makers put a lot of intensive effort into development
of autonomous driving systems. Since visual information is the main modality used by human
driver, a camera mounted on moving platform is very important kind of sensor, and various
computer vision algorithms to handle vehicle surrounding situation are under intensive
research. Our final goal is to develop a vision based lane detection system with ability to
handle various types of road shapes, working on both structured and unstructured roads, ideally
under presence of shadows. This paper presents a modified road region segmentation algorithm
based on sequential Monte-Carlo estimation. Detailed description of the algorithm is given,
and evaluation results show that the proposed algorithm outperforms the segmentation
algorithm developed as a part of our previous work, as well as an conventional algorithm based
on colour histogram.
IRJET-Road Detection from Satellite ImagesIRJET Journal
This document summarizes a research paper about detecting roads from satellite images. It begins with an abstract stating that the goal is to propose a more accurate model for road detection compared to current methods using Google Maps, which can be outdated or inaccurate. It then provides background on roads in India and issues with existing navigation systems sometimes showing roads that are under construction. The paper proposes using Otsu's method and morphological operations on input images to better detect roads from satellite imagery. It also reviews related work on using neural networks and color features for road detection from aerial/satellite images.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
License plate recognition system is one of the core technologies in intelligent traffic control. In this paper, a new and tunable algorithm which can detect multiple license plates in high resolution applications is proposed. The algorithm aims at investigation into and identification of the novel Iranian and some European countries plate, characterized by both inclusion of blue area on it and its geometric shape. Obviously, the suggested algorithm contains suitable velocity due to not making use of heavy pre-processing operation such as image-improving filters, edge-detection operation and omission of noise at the beginning stages. So, the recommended method of ours is compatible with model-adaptation, i.e., the very blue section of the plate so that the present method indicated the fact that if several plates are included in the image, the method can successfully manage to detect it. We evaluated our method on the two Persian single vehicle license plate data set that we obtained 99.33, 99% correct recognition rate respectively. Further we tested our algorithm on the Persian multiple vehicle license plate data set and we achieved 98% accuracy rate. Also we obtained approximately 99% accuracy in character recognition stage.
Simulation of Urban Mobility (Sumo) For Evaluating Qos Parameters For Vehicul...iosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study on Single Camera Based ANPR System for Improvement of Vehicle Number ...journal ijrtem
This document summarizes a study on a single camera-based automatic number plate recognition (ANPR) system to recognize vehicle license plates on multi-lane roads. It proposes a character extraction algorithm using connected vertical and horizontal edge segments to improve recognition rates. The algorithm detects character edge patterns, extracts components by cumulatively labeling edges, and enhances images through contrast adaptive binarization. An ANPR system was installed on a 3-lane test road and achieved a detection rate of 84.6%, though some errors occurred with specific vehicle models or character differences. Further research is needed to handle low-visibility conditions and improve accuracy.
This document presents a system for detecting stop line violations in Myanmar using license plate detection. The system first preprocesses video frames, then uses projection analysis and filtering to localize license plates. It extracts candidate plates based on geometric features like area and aspect ratio. Finally, it makes a decision by comparing the Y-coordinate of the license plate to the Y-coordinate of the stop line - if the plate is above the line, it is a violation. The system was tested on 17 hours of video from Myanmar intersections and showed accuracy in determining violated versus non-violated vehicles.
Design Approach for a Novel Traffic Sign Recognition System by Using LDA and ...IJERA Editor
This research paper highlights the problems that are encountered in a typical Traffic Sign Recognition System
like incorrect interpretation of a particular traffic sign which is observed by a driver while driving a vehicle
causing misunderstanding thereby resulting in road accidents. The visibility is affected by many environmental
factors such as smoke, rain, fog, humid weather, dust etc. and it is very difficult to understand the traffic signs in
this situations, causing misinterpretations of the particular traffic sign and resulting in road accidents. In order to
avoid this condition, a novel method of recognizing traffic signs is developed which take into consideration the
color and shape of the traffic sign. A algorithm called as Linear Discriminant Analysis (LDA) is used for
classification of different groups of traffic signs which are predefined by a particular set of features after the
process of Image Segmentation. The images are segmented by using the color and shape features of an image
and the features are extracted by using the Haar Transform and then the classification of images is done by using
Linear Discriminant Analysis Algorithm. Finally the GUI of traffic sign images is prepared by using the
software tool called as MATLAB.Our main objective is to recognize partially occluded traffic signs in a cloudy
environment by using LDA and to make an efficient Traffic Sign Detection system which will be capable of
recognizing and classifying any kind of known traffic sign from the other traffic signs by considering the color
and shape of the traffic sign on the basis of supervised classification of the training data so that any error which
results in a faulty detection or incorrect detection of traffic sign can be eliminated.
Knowledge-based Expert System for Route Selection of Road AlignmentUCSI University
This document summarizes a study that developed an expert system using GIS to assist in highway geometric design and route selection. The study integrated GIS to obtain contour map data for the design area, which was then used as input for the expert system. The expert system evaluates potential route alignments based on economic, technical, environmental, and other factors to recommend the best alignment. It aims to help engineers efficiently evaluate alternatives and make recommendations for highway geometric design. The methodology combines GIS for spatial data with an expert system that applies rules to select an optimal route alignment.
The document discusses various methods used for origin-destination surveys in traffic engineering. It describes roadside interview surveys, home interview surveys, telephone surveys, taxi surveys, and other methods for collecting data on vehicle origins, destinations, routes, and passengers. It also discusses analyzing the data for purposes like evaluating existing routes, locating new roads or parking, and regulating vehicle movement.
Path planning has been an important aspect in the development of autonomous cars in which path planning is used to find a collision-free path for the car to traverse from a starting point Sp to a target point Tp. The main criteria for a good path planning algorithm include the capability of producing the shortest path with a low computation time. Low computation time makes the autonomous car able to re-plan a new collision-free path to avoid accident. However, the main problem with most path planning methods is their computation time increases as the number of obstacles in the environment increases. In this paper, an algorithm based on visibility graph (VG) is proposed. In the proposed algorithm, which is called Equilateral Space Oriented Visibility Graph (ESOVG), the number of obstacles considered for path planning is reduced by introducing a space in which the obstacles lie. This means the obstacles located outside the space are ignored for path planning. From simulation, the proposed algorithm has an improvement rate of up to 90% when compared to VG. This makes the algorithm is suitable to be applied in real-time and will greatly accelerate the development of autonomous cars in the near future.
Path planning has been an important aspect in the development of autonomous cars in which path planning is used to find a collision-free path for the car to traverse from a starting point Sp to a target point Tp. The main criteria for a good path planning algorithm include the capability of producing the shortest path with a low computation time. Low computation time makes the autonomous car able to re-plan a new collision-free path to avoid accident. However, the main problem with most path planning methods is their computation time increases as the number of obstacles in the environment increases. In this paper, an algorithm based on visibility graph (VG) is proposed. In the proposed algorithm, which is called Equilateral Space Oriented Visibility Graph (ESOVG), the number of obstacles considered for path planning is reduced by introducing a space in which the obstacles lie. This means the obstacles located outside the space are ignored for path planning. From simulation, the proposed algorithm has an improvement rate of up to 90% when compared to VG. This makes the algorithm is suitable to be applied in real-time and will greatly accelerate the development of autonomous cars in the near future.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET - Analysis of Different Arbitration Algorithms for Amba Ahb Bus Protoco...IRJET Journal
This document analyzes and compares different arbitration algorithms for the Advanced Microcontroller Bus Architecture (AMBA) Advanced High-Performance Bus (AHB) protocol used in System on Chip (SoC) designs. It describes the AMBA specification and AHB bus. It then examines three arbitration algorithms - static fixed priority, round robin, and modified round robin. Simulation results show the modified round robin algorithm provides the fastest response time to bus requests while using more logic than the other methods. Overall, the modified round robin algorithm is concluded to be the most efficient approach for handling multiple concurrent bus requests in terms of speed and performance.
Cyber-physical system based on image recognition to improve traffic flow: A cas...IJECEIAES
This document summarizes a research paper that proposes a cyber-physical system using image recognition to control access to exclusive traffic lanes and reduce congestion. The system uses a Raspberry Pi camera and infrared sensors to capture images of license plates when vehicles enter the lanes. It analyzes the images using neural networks and databases to identify the plates and check if the vehicles have permission. If not, it notifies the owners by email or SMS with evidence photos. The system was tested outdoors with some accuracy issues related to license plate formatting and image angles. Overall, the research demonstrates using image recognition and databases in a cyber-physical system to automate traffic lane enforcement.
The document summarizes a preliminary design project to redesign the intersection of Yellowstone Highway and Trejo Street in Rexburg, Idaho. Three design alternatives were proposed and evaluated to address illegal through traffic and lack of a left turn lane. Alternative 1 proposed widening Yellowstone Highway and restriping Trejo Street. Although it did not improve traffic flow, it was deemed the best option due to increased safety and low cost. AutoCAD was used to design the road alignments, cross sections, and calculate the pavement thickness required for Alternative 1.
IMPORTANCE OF REALISTIC MOBILITY MODELS FOR VANET NETWORK SIMULATIONIJCNCJournal
In the performance evaluation of a protocol for a vehicular ad hoc network, the protocol should be tested under a realistic conditions including, representative data traffic models, and realistic movements of the mobile nodes which are the vehicles (i.e., a mobility model). This work is a comparative study between two mobility models that are used in the simulations of vehicular networks, i.e., MOVE (MObility model generator for VEhicular networks) and CityMob, a mobility pattern generator for VANET. We describe several mobility models for VANET simulations.
In this paper we aim to show that the mobility models can significantly affect the simulation results in VANET networks. The results presented in this article prove the importance of choosing a suitable real world scenario for performances studies of routing protocols in this kind of network.
The document outlines the steps involved in highway route location, which includes reconnaissance, preliminary, and final location surveys. The reconnaissance survey evaluates feasibility of corridor routes based on topography, traffic, land use, environment, and economics. The preliminary survey determines horizontal and vertical alignments and evaluates routes for cost, environment, safety, and cost-benefit. The final survey fixes the center line and collects additional data like cross-sections and elevations for working drawings. The overall process aims to find a location that minimizes costs while considering traffic needs and impacts.
A computer vision-based lane detection approach for an autonomous vehicle usi...Md. Faishal Rahaman
Lane detection systems play a critical role in ensuring safe and secure driving by alerting the driver of lane departures. Lane detection may also save passengers' lives if they go off the road owing to driver distraction. The article presents a three-step approach for detecting lanes on high-speed video pictures in real-time and invariant lighting. The first phase involves doing appropriate prepossessing, such as noise reduction, RGB to grey-scale conversion, and binarizing the input picture. Then, a polygon area in front of the vehicle is picked as the zone of interest to accelerate processing. Finally, the edge detection technique is used to acquire the image's edges in the area of interest, and the Hough transform is used to identify lanes on both sides of the vehicle. The suggested approach was implemented using the IROADS database as a data source. The recommended method is effective in various daylight circumstances, including sunny, snowy, and rainy days, as well as inside tunnels. The proposed approach processes frame on average in 28 milliseconds and have a detection accuracy of 96.78 per cent, as shown by implementation results. This article aims to provide a simple technique for identifying road lines on high-speed video pictures utilizing the edge feature.
Traffic flow measurement for smart traffic light system designTELKOMNIKA JOURNAL
Determining congestions on intersection roads can significantly improve the performance of a traffic light system. One of the everyday problems on our roads nowadays is the unbalanced traffic on different roads. The blind view of roads and the dependency on the conventional timer-based traffic light systems can cause unnecessary delays on some arterial roads on expense of offering a needless extra pass time on some other secondary minor roads. In this paper, a foreground extraction model has been built in MATLAB platform to measure the congestions on the different roads constructing an intersection. Results show a satisfactory performance in terms of accuracy in counting cars and in consequence reducing the wait time on some major roads. System was tested under different weather and lighting conditions, and results were adequately promising.
Application of neural network method for road crack detectionTELKOMNIKA JOURNAL
The study presents a road pavement crack detection system by extracting
picture features then classifying them based on image features. The applied
feature extraction method is the gray level co-occurrence matrices (GLCM).
This method employs two order measurements. The first order utilizes
statistical calculations based on the pixel value of the original image alone,
such as variance, and does not pay attention to the neighboring pixel
relationship. In the second order, the relationship between the two pixel-pairs
of the original image is taken into account. Inspired by the recent success
in implementing Supervised Learning in computer vision, the applied method
for classification is artificial neural network (ANN). Datasets, which are used
for evaluation are collected from low-cost smart phones. The results show that
feature extraction using GLCM can provide good accuracy that is equal
to 90%.
This document provides a literature review of lane detection techniques for real-time road lane detection systems. It discusses how lane detection is an important aspect of intelligent transportation systems and driver assistance systems. The review covers various existing approaches to lane detection including image processing methods, edge detection, the Hough transform, and lane departure recognition. It identifies some limitations in existing methods, such as poor performance under difficult environmental conditions or on curved roads. The document proposes developing a new lane detection method to address these limitations and improve accuracy for real-time applications.
License plate recognition system is one of the core technologies in intelligent traffic control. In this paper, a new and tunable algorithm which can detect multiple license plates in high resolution applications is proposed. The algorithm aims at investigation into and identification of the novel Iranian and some European countries plate, characterized by both inclusion of blue area on it and its geometric shape. Obviously, the suggested algorithm contains suitable velocity due to not making use of heavy pre-processing operation such as image-improving filters, edge-detection operation and omission of noise at the beginning stages. So, the recommended method of ours is compatible with model-adaptation, i.e., the very blue section of the plate so that the present method indicated the fact that if several plates are included in the image, the method can successfully manage to detect it. We evaluated our method on the two Persian single vehicle license plate data set that we obtained 99.33, 99% correct recognition rate respectively. Further we tested our algorithm on the Persian multiple vehicle license plate data set and we achieved 98% accuracy rate. Also we obtained approximately 99% accuracy in character recognition stage.
Simulation of Urban Mobility (Sumo) For Evaluating Qos Parameters For Vehicul...iosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study on Single Camera Based ANPR System for Improvement of Vehicle Number ...journal ijrtem
This document summarizes a study on a single camera-based automatic number plate recognition (ANPR) system to recognize vehicle license plates on multi-lane roads. It proposes a character extraction algorithm using connected vertical and horizontal edge segments to improve recognition rates. The algorithm detects character edge patterns, extracts components by cumulatively labeling edges, and enhances images through contrast adaptive binarization. An ANPR system was installed on a 3-lane test road and achieved a detection rate of 84.6%, though some errors occurred with specific vehicle models or character differences. Further research is needed to handle low-visibility conditions and improve accuracy.
This document presents a system for detecting stop line violations in Myanmar using license plate detection. The system first preprocesses video frames, then uses projection analysis and filtering to localize license plates. It extracts candidate plates based on geometric features like area and aspect ratio. Finally, it makes a decision by comparing the Y-coordinate of the license plate to the Y-coordinate of the stop line - if the plate is above the line, it is a violation. The system was tested on 17 hours of video from Myanmar intersections and showed accuracy in determining violated versus non-violated vehicles.
Design Approach for a Novel Traffic Sign Recognition System by Using LDA and ...IJERA Editor
This research paper highlights the problems that are encountered in a typical Traffic Sign Recognition System
like incorrect interpretation of a particular traffic sign which is observed by a driver while driving a vehicle
causing misunderstanding thereby resulting in road accidents. The visibility is affected by many environmental
factors such as smoke, rain, fog, humid weather, dust etc. and it is very difficult to understand the traffic signs in
this situations, causing misinterpretations of the particular traffic sign and resulting in road accidents. In order to
avoid this condition, a novel method of recognizing traffic signs is developed which take into consideration the
color and shape of the traffic sign. A algorithm called as Linear Discriminant Analysis (LDA) is used for
classification of different groups of traffic signs which are predefined by a particular set of features after the
process of Image Segmentation. The images are segmented by using the color and shape features of an image
and the features are extracted by using the Haar Transform and then the classification of images is done by using
Linear Discriminant Analysis Algorithm. Finally the GUI of traffic sign images is prepared by using the
software tool called as MATLAB.Our main objective is to recognize partially occluded traffic signs in a cloudy
environment by using LDA and to make an efficient Traffic Sign Detection system which will be capable of
recognizing and classifying any kind of known traffic sign from the other traffic signs by considering the color
and shape of the traffic sign on the basis of supervised classification of the training data so that any error which
results in a faulty detection or incorrect detection of traffic sign can be eliminated.
Knowledge-based Expert System for Route Selection of Road AlignmentUCSI University
This document summarizes a study that developed an expert system using GIS to assist in highway geometric design and route selection. The study integrated GIS to obtain contour map data for the design area, which was then used as input for the expert system. The expert system evaluates potential route alignments based on economic, technical, environmental, and other factors to recommend the best alignment. It aims to help engineers efficiently evaluate alternatives and make recommendations for highway geometric design. The methodology combines GIS for spatial data with an expert system that applies rules to select an optimal route alignment.
The document discusses various methods used for origin-destination surveys in traffic engineering. It describes roadside interview surveys, home interview surveys, telephone surveys, taxi surveys, and other methods for collecting data on vehicle origins, destinations, routes, and passengers. It also discusses analyzing the data for purposes like evaluating existing routes, locating new roads or parking, and regulating vehicle movement.
Path planning has been an important aspect in the development of autonomous cars in which path planning is used to find a collision-free path for the car to traverse from a starting point Sp to a target point Tp. The main criteria for a good path planning algorithm include the capability of producing the shortest path with a low computation time. Low computation time makes the autonomous car able to re-plan a new collision-free path to avoid accident. However, the main problem with most path planning methods is their computation time increases as the number of obstacles in the environment increases. In this paper, an algorithm based on visibility graph (VG) is proposed. In the proposed algorithm, which is called Equilateral Space Oriented Visibility Graph (ESOVG), the number of obstacles considered for path planning is reduced by introducing a space in which the obstacles lie. This means the obstacles located outside the space are ignored for path planning. From simulation, the proposed algorithm has an improvement rate of up to 90% when compared to VG. This makes the algorithm is suitable to be applied in real-time and will greatly accelerate the development of autonomous cars in the near future.
Path planning has been an important aspect in the development of autonomous cars in which path planning is used to find a collision-free path for the car to traverse from a starting point Sp to a target point Tp. The main criteria for a good path planning algorithm include the capability of producing the shortest path with a low computation time. Low computation time makes the autonomous car able to re-plan a new collision-free path to avoid accident. However, the main problem with most path planning methods is their computation time increases as the number of obstacles in the environment increases. In this paper, an algorithm based on visibility graph (VG) is proposed. In the proposed algorithm, which is called Equilateral Space Oriented Visibility Graph (ESOVG), the number of obstacles considered for path planning is reduced by introducing a space in which the obstacles lie. This means the obstacles located outside the space are ignored for path planning. From simulation, the proposed algorithm has an improvement rate of up to 90% when compared to VG. This makes the algorithm is suitable to be applied in real-time and will greatly accelerate the development of autonomous cars in the near future.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET - Analysis of Different Arbitration Algorithms for Amba Ahb Bus Protoco...IRJET Journal
This document analyzes and compares different arbitration algorithms for the Advanced Microcontroller Bus Architecture (AMBA) Advanced High-Performance Bus (AHB) protocol used in System on Chip (SoC) designs. It describes the AMBA specification and AHB bus. It then examines three arbitration algorithms - static fixed priority, round robin, and modified round robin. Simulation results show the modified round robin algorithm provides the fastest response time to bus requests while using more logic than the other methods. Overall, the modified round robin algorithm is concluded to be the most efficient approach for handling multiple concurrent bus requests in terms of speed and performance.
Cyber-physical system based on image recognition to improve traffic flow: A cas...IJECEIAES
This document summarizes a research paper that proposes a cyber-physical system using image recognition to control access to exclusive traffic lanes and reduce congestion. The system uses a Raspberry Pi camera and infrared sensors to capture images of license plates when vehicles enter the lanes. It analyzes the images using neural networks and databases to identify the plates and check if the vehicles have permission. If not, it notifies the owners by email or SMS with evidence photos. The system was tested outdoors with some accuracy issues related to license plate formatting and image angles. Overall, the research demonstrates using image recognition and databases in a cyber-physical system to automate traffic lane enforcement.
The document summarizes a preliminary design project to redesign the intersection of Yellowstone Highway and Trejo Street in Rexburg, Idaho. Three design alternatives were proposed and evaluated to address illegal through traffic and lack of a left turn lane. Alternative 1 proposed widening Yellowstone Highway and restriping Trejo Street. Although it did not improve traffic flow, it was deemed the best option due to increased safety and low cost. AutoCAD was used to design the road alignments, cross sections, and calculate the pavement thickness required for Alternative 1.
IMPORTANCE OF REALISTIC MOBILITY MODELS FOR VANET NETWORK SIMULATIONIJCNCJournal
In the performance evaluation of a protocol for a vehicular ad hoc network, the protocol should be tested under a realistic conditions including, representative data traffic models, and realistic movements of the mobile nodes which are the vehicles (i.e., a mobility model). This work is a comparative study between two mobility models that are used in the simulations of vehicular networks, i.e., MOVE (MObility model generator for VEhicular networks) and CityMob, a mobility pattern generator for VANET. We describe several mobility models for VANET simulations.
In this paper we aim to show that the mobility models can significantly affect the simulation results in VANET networks. The results presented in this article prove the importance of choosing a suitable real world scenario for performances studies of routing protocols in this kind of network.
The document outlines the steps involved in highway route location, which includes reconnaissance, preliminary, and final location surveys. The reconnaissance survey evaluates feasibility of corridor routes based on topography, traffic, land use, environment, and economics. The preliminary survey determines horizontal and vertical alignments and evaluates routes for cost, environment, safety, and cost-benefit. The final survey fixes the center line and collects additional data like cross-sections and elevations for working drawings. The overall process aims to find a location that minimizes costs while considering traffic needs and impacts.
A computer vision-based lane detection approach for an autonomous vehicle usi...Md. Faishal Rahaman
Lane detection systems play a critical role in ensuring safe and secure driving by alerting the driver of lane departures. Lane detection may also save passengers' lives if they go off the road owing to driver distraction. The article presents a three-step approach for detecting lanes on high-speed video pictures in real-time and invariant lighting. The first phase involves doing appropriate prepossessing, such as noise reduction, RGB to grey-scale conversion, and binarizing the input picture. Then, a polygon area in front of the vehicle is picked as the zone of interest to accelerate processing. Finally, the edge detection technique is used to acquire the image's edges in the area of interest, and the Hough transform is used to identify lanes on both sides of the vehicle. The suggested approach was implemented using the IROADS database as a data source. The recommended method is effective in various daylight circumstances, including sunny, snowy, and rainy days, as well as inside tunnels. The proposed approach processes frame on average in 28 milliseconds and have a detection accuracy of 96.78 per cent, as shown by implementation results. This article aims to provide a simple technique for identifying road lines on high-speed video pictures utilizing the edge feature.
Traffic flow measurement for smart traffic light system designTELKOMNIKA JOURNAL
Determining congestions on intersection roads can significantly improve the performance of a traffic light system. One of the everyday problems on our roads nowadays is the unbalanced traffic on different roads. The blind view of roads and the dependency on the conventional timer-based traffic light systems can cause unnecessary delays on some arterial roads on expense of offering a needless extra pass time on some other secondary minor roads. In this paper, a foreground extraction model has been built in MATLAB platform to measure the congestions on the different roads constructing an intersection. Results show a satisfactory performance in terms of accuracy in counting cars and in consequence reducing the wait time on some major roads. System was tested under different weather and lighting conditions, and results were adequately promising.
Application of neural network method for road crack detectionTELKOMNIKA JOURNAL
The study presents a road pavement crack detection system by extracting
picture features then classifying them based on image features. The applied
feature extraction method is the gray level co-occurrence matrices (GLCM).
This method employs two order measurements. The first order utilizes
statistical calculations based on the pixel value of the original image alone,
such as variance, and does not pay attention to the neighboring pixel
relationship. In the second order, the relationship between the two pixel-pairs
of the original image is taken into account. Inspired by the recent success
in implementing Supervised Learning in computer vision, the applied method
for classification is artificial neural network (ANN). Datasets, which are used
for evaluation are collected from low-cost smart phones. The results show that
feature extraction using GLCM can provide good accuracy that is equal
to 90%.
This document provides a literature review of lane detection techniques for real-time road lane detection systems. It discusses how lane detection is an important aspect of intelligent transportation systems and driver assistance systems. The review covers various existing approaches to lane detection including image processing methods, edge detection, the Hough transform, and lane departure recognition. It identifies some limitations in existing methods, such as poor performance under difficult environmental conditions or on curved roads. The document proposes developing a new lane detection method to address these limitations and improve accuracy for real-time applications.
IRJET- Road Recognition from Remote Sensing Imagery using Machine LearningIRJET Journal
This document discusses a framework for recognizing roads from remote sensing imagery using machine learning. It proposes using a convolutional neural network (CNN) trained on large amounts of reference data to initially classify imagery into classification maps, then refining the CNN on a small amount of accurately labeled data. A series of experiments in MATLAB show this two-step training approach considers a large amount of context to provide fine-grained classification maps while addressing issues with imperfect training data. The document also reviews several existing methods for road extraction from remote sensing imagery and their limitations.
A brawny multicolor lane detection method to indian scenarioseSAT Journals
Abstract Lane detection is an essential component of Advanced Driver Assistance System. The cognition on the roads is increasing day by day due to increase in the four wheelers on the road. The cognition coupled with ignorance towards road rules is contributing to road accidents. The lane marking violence is one of the major causes for accidents on highways in India. In this work we have designed and implemented an automatic lane marking violence detection algorithm in real time. The HSV color-segmentation based approach is verified for both white lanes and yellow lanes in Indian context. Various comparative experimental results show that the proposed approach is very effective in the lane detection and can be implemented in real- time. Index Terms: - Color segmentation; HSV; Edge orientation; connected components
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
LANE CHANGE DETECTION AND TRACKING FOR A SAFE-LANE APPROACH IN REAL TIME VISI...cscpconf
Image sequences recorded with cameras mounted in a moving vehicle provide information
about the vehicle’s environment which has to be analysed in order to really support the driver
in actual traffic situations. One type of information is the lane structure surrounding the vehicle.
Therefore, driver assistance functions which make explicit use of the lane structure represented
by lane borders and lane markings is to be analysed. Lane analysis is performed on the road
region to remove road pixels. Only lane markings are the interests for the lane detection
process. Once the lane boundaries are located, the possible edge pixels are scanned to
continuously obtain the lane model. The developed system can reduce the complexity of vision
data processing and meet the real time requirements.
TRAFFIC-SIGN RECOGNITION FOR AN INTELLIGENT VEHICLE/DRIVER ASSISTANT SYSTEM U...cseij
The document describes a traffic sign recognition system using HOG features and a k-NN classifier. The system first detects signs using RGB color thresholding and shape analysis. It then extracts HOG features from the segmented images. Finally, it classifies the signs using a k-NN classifier. The system was tested on a database of 200 traffic sign images under varying lighting conditions. It achieved a classification accuracy of 63%. The proposed approach provides robust traffic sign recognition while being invariant to scale, rotation, and illumination changes.
A survey on road extraction from color image using vectorizationeSAT Journals
Abstract Road extraction can be defined as extracting the roads from an image by accessing it through the features of road. Road information can be extracted from images in three ways: manual extraction, semi-automated extraction and fully automated detection. In this paper we review various research papers of road extraction methods. Most of the work focused on road extraction from grayscale images. Our proposed approach is to extract road from color image using vectorization. Vectorization is performed using canny edge detection and CDT (Constrained Delaunay Triangulation) and then grouped resulting triangles into polygons to make up vector image. A sequence of pre-processing steps is applied to get better quality of image and to produce extended road network before vectorization. At the end, skeletonization is used to transform the components of digital image into original components.
Keywords: vectorization, constrained Delaunay triangulation, canny edge detection, skeletonization,
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This paper proposes a semi-automatic approach to extract roads from high-resolution remote sensing images. The approach uses Canny edge detection to initially segment roads. Adjacent segments are then merged using Full Lambda Schedule. Support vector machine (SVM) classification is performed on the image to extract roads. Morphological operators like dilation and erosion are finally applied to remove noise and improve road detection accuracy. The method is evaluated on WorldView, QuickBird, and UltraCam images and shows high accuracy for extracting different road types.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Enhancement performance of road recognition system of autonomous robots in sh...sipij
This document summarizes a research paper that proposed an algorithm to improve road recognition for autonomous vehicles in shadow scenarios. The researchers conducted experiments to test their algorithm's performance on key metrics like true positive rate and error rate. Their algorithm first converted images to HSV color space to detect shadows, then used normalized difference index and morphological operations to eliminate shadow effects before segmentation and classification. Test results showed their algorithm enhanced road recognition in the presence of shadows, advancing autonomous vehicle navigation capabilities.
1) The document proposes techniques for real-time Indian road analysis using image processing and computer vision, including lane detection, pothole detection, object detection from video, and road sign classification.
2) It discusses using the Hough transform for lane detection, color segmentation and shape modeling with thin spline transformation for road sign classification, and k-means clustering for pothole detection.
3) The techniques are tested on images and video streams from Indian roads to show they can be used for real-time analysis in an automated driver guidance system.
Recognition of road markings from street-level panoramic images for automated...Thomas Woudsma
This paper presents a road-marking recognition pipeline operating on street-level panoramic images. On a large dataset of 84,387 images, the full processing pipeline achieves detection rates of 85%, 92% and 80% for crosswalks, block- and give-way markings, respectively, with a positioning error smaller than 0.6m. This shows that the presented system is performing sufficiently well for generating road-marking maps.
This document provides an overview of image processing techniques for traffic applications. It discusses automatic lane finding using color-based and texture-based segmentation as well as feature-driven approaches. Object detection methods like thresholding, edge detection using Canny operator, and background differencing are also covered. Additionally, the document proposes an emergency vehicle detection system using red beacon detection and frequency analysis to override normal traffic light patterns when an emergency vehicle is detected.
Road Sign Detection and Recognition by using Local Energy Based Shape Histogr...CSCJournals
The paper describes an efficient approach towards road sign detection and recognition system. The system is divided into three sections I) Segmentation of road traffic signs using HSV colour space under varying lighting conditions II) Shape Classification by using contourlet transform considering occlusion and rotation of the candidate sign III) and the Recognition of the road traffic sign is performed by using Local Energy based shape histogram. The algorithm described in this paper is robust enough to detect and recognize road sign under varying weather, occlusion, rotation and scaling conditions.
Vehicle detection and tracking techniques a concise reviewsipij
Vehicle detection and tracking applications play an important role for civilian and military applications
such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection
process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic
analysis and vehicle categorizing objectives and may be implemented under different environments
changes. In this review, we present a concise overview of image processing methods and analysis tools
which used in building these previous mentioned applications that involved developing traffic surveillance
systems. More precisely and in contrast with other reviews, we classified the processing methods under
three categories for more clarification to explain the traffic systems.
Street Mark Detection Using Raspberry PI for Self-driving SystemTELKOMNIKA JOURNAL
Self driving is an autonomous vehicle that can follow the road with less human intervention. The development of self driving utilizes various methods such as radar, lidar, GPS, camera, or combination of them. In this research, street mark detection system was designed using webcam and raspberry-pi mini computer for processing the image. The image was processed by HSV color filtering method. The processing rate of this algorithm was 137.98 ms correspondinig to 7.2 FPS. The self-driving prototype was found to be working optimally for “hue” threshold of 0-179, “saturation” threshold of 0-30, and “value” threshold of 200-255. Street mark detection has been obtained from the coordinates of street mark object which had range 4-167 on x axis and 4-139 on y axis. As a result, we have successfully built the street mark detection by COG method more effectively and smoothly in detection in comparison with Hough transform method.
Similar to Road markers classification using binary scanning and slope contours (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
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.
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.
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.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
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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classify road markers into four types namely double solid, single solid, merged, and dashed
using a periodic histogram and ego-localization.
Paula et al [18-20] had worked on the five types of road marker. A detection method
based on Bayesian approach for classifying the road markers has been proposed. Although it is
able to classify five types of road markers, the method suffers outlier problems especially at
transitions between two types of markers. The results of the classification were found to have
changed abruptly on each frame, which caused inconsistent results while driving.
Mathibela et al. [21] concentrate on the road markers found at the urban area and able to
classify seven types, namely; double boundary, single boundary, zig-zag, separator, boxed
junction, intersectionand special lanes by using a unique set of geometric features which
function within a probabilistic RUSBoost and Conditional Random Field (CRF) network.
However, this research is mainly carried out on a static image. The latest publication from
Zamani et al. [22, 23] using the features extracted from the road lane, and fed into the Artificial
Neural Network (ANN). The initial research starts with two types of lane markers and later
expanded to five types of markers classification [24] which the features extracted from
the customized Region of Interest (ROI). Nevertheless, the algorithm requires temporal values
from to lot of image sequence before it can make a decision on type’s transition due to its small
ROI. This may lags the information to alert the drivers. The aforementioned methods have been
proposed to address different types of road markers subject to the physical features of the roads
including the dimension, colour and size of the markers. In our research, the types of road
markers which are normally found at the non-urban carriageway are classified using a machine
vision system attached to a car. The road markers include solid, dashed, dashed-solid,
double-solid and solid-dashed, as shown in Figure 1.
(a) (b) (c) (d) (e)
Figure 1. Road Markers found at two lane single carriageway (a) dashed (D)
(b) dashed-solid (DS) (c) double solid (DD) (d) solid-dashed (SD) (e) single solid (SS)
2. Research Method
The proposed classification process is using a two-layer classifier as shown as in
Figure 2. First layer will classify three classes of markers whereas the next layer will classify
another two more classes. The first layer is using the scanning the binary image to get
the percentage of the binary transition and the second layer classifier is using the slope value
from the two contours detected in the same image. Before all these process can be done,
the Region of Interest (ROI) will be determined from the video image, and this would require
the camera location to be adjusted at the front of the camera.
Figure 2. Two-layer classifier
First Layer Classifier
SINGLE SOLID DASHEDDOUBLE
SOLID
Second layer Classifier
SOLID-
DASHED
DASHED-
SOLID
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2.1. Camera Setup for Region of Interest (ROI) selection
A new method in selecting the ROI based on its calibrated camera’s height and Field of
View (FOV) are used. For the initial setup, a camera with a resolution of 1280x720 located at
the center of the lane, is calibrated on its position with its FOV adjusted towards the planar road
surface captured by the camera as shown in Figures 3 (a) and 3 (b). To calibrate the position of
the camera, two horizontal lines, h1 and h2, are set on the FOV to equally divide into 3 sections
as in Figure 3 (c), where the h1 horizontal line in the image must cross the intersection point
between the rightmost lane, the off-road region and the border of the image to maximize
the ROI.
The ROI selection is then applied to the video and the output for the ROI is labelled as
r(x,y) as in Figure 4 which contains the information of the marker, by dividing the image into two
halves before selecting two third of from the right, as indicated by the red box, which is bordered
by the two horizontal lines. With this method, the ROI contains the largest visible road marker of
the image.
(a) (b)
(c)
Figure 3. The camera setup (a) camera position (b) 3D camera coordinate system
(c) camera height adjustment with h1 and h2 horizontal lines
Figure 4. ROI selection
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The ROI, r(x,y) which is in two dimension (2D) will represents the video images in pixels
and will go through the pre-processing prior to the feature extraction and classification process.
The raw image in Red Green Blue (RGB) video images will be filtered through the Gaussian
filter for 2D as in (1). The parameter x is the horizontal length from the origin and y is the vertical
length from the origin and σ is the standard deviation of the Gaussian distribution [25].
𝐺(𝑥, 𝑦) =
1
2𝜋𝜎2
𝑒
−
𝑥2+𝑦2
2𝜎2
(1)
This Gaussian operation will reduce the noise generated in the segmentation process to
produce a good binary image for thresholding operation on Hue, Saturation and Luminance
format (HSL). In this process, the normal RGB colour image is converted to HSL. Since
the typically white lane marker is on a grey background, the luminance value is adjusted as
the threshold, use to convert the image to binary format.
2.2. Extracting The Road Marking Cues
After investigating the best ROI for processing the image, the next step is to extract
the road marking cues. The ROI will be scanned from the top to the bottom and from the left to
the right as shown by the arrows in Figure 5 (b). A binary column vector 𝑓𝑥, which is extracted
from the x-th column of the binary image having binary pixels of 𝑓(𝑥, 𝑦) ∈ {0,1}
for 𝑥 = 0,1, ⋯ , 𝑁𝑥 and 𝑦 = 0,1, ⋯ , 𝑁𝑦, is defined as follows
𝑓𝑥[𝑦] = {
0, 𝑓(𝑥, 𝑦) = 0
1, 𝑓(𝑥, 𝑦) = 1
(2)
This binary column vector tells how many white pixels found on the x-th column. When
there are road markers crossing the column, the number of white pixels will increase. In order to
reduce the computational complexity, not all columns will be read, or horizontally scanned over
the image. It is sufficient to horizontally scan the image for obtaining the values over a constant
horizontal interval of δx as in Figure 5 (b), which is the number of pixel columns of the binary
image which are skipped and not scanned, as long as the road marker can be correctly classify.
Example of scanning process on the image shown in Figure 5, with various type of road
markers and different value of δx.
By using an example of a 420x260 pixel size for an ROI, it will requires 41 times of lines
scanning using with interval size of δx=10 pixels, producing 41 binary column vectors 𝒇 𝑥, where
𝑁𝑥 = 260 (the Height size). Every binary column vector, 𝒇 𝑥 will be processed to detect and
count the number of transitions, 𝑁𝑡 from 0 to 1 and from 1 to 0 in it by using a bitwise exclusive
OR operation between itself, 𝒇 𝑥 and 𝒇 𝑥𝑟, which is a vector produced from shifting 𝒇 𝑥 by one
binary bit or element as shown in an example.
- bitwise shift to the right by adding 0 to the first bit and shift the rest to the right
𝒇 𝑥
𝑻
= [0001111000], 𝒇 𝑥𝑟
𝑻
= [0000111100]
- perform the exclusive OR with original value resulting only transition bits are left
- 𝒇 𝑡 = 𝒇 𝑥
𝑻
⨁𝒇 𝑥𝑟
𝑻
= [0001111000] ⊕ [0000111100] = [0001000100]
- count the number of transitions on every scan line or binary column vector
𝑁𝑡 = ∑ 𝑓𝑡[𝑦]
𝑁 𝑥
𝑦=1 = 2
The numbers of transitions in each binary column vector obtained from the scanning
line are saved into a vector, 𝒇 𝑵 𝒕
. An example of this vector produced from scanning an ROI with
an interval size of 10 pixels is given in Figure 6. The number of transitions are in each binary
column vector is observed to be either 0, 2 or 4. For the road marker SS, there are at most 2
transitions in each vector whereas for D, the number of transitions is either 0 or 2. For DD,
the number of transitions is 4, whereas for SD and DS, the number of transitions is either 2 or 4.
To differentiate between SD and DS, additional features will be extracted to differentiate
between these two classes.
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(a) (b)
(c) (d)
Figure 5. Example of scanning image with different interval size with different road markers
Vector 𝒇𝒕 will be processed where the number of transitions will be grouped and
the percentage of 𝑁𝑡, or the numbers of transitions will be stored in vector 𝒇 𝑵 𝒕
as formulated by
𝑁𝑡
p
in (3), which is applied to calculate the percentage of 𝑁𝑡 for five different classes as
tabulated in Table 1. According to this table, 𝑁1 = 0, 𝑁2 = 2 and 𝑁3 = 4.
𝑁𝑡
p
=
Frequency of 𝑁𝑡
Total frequency
× 100% (3)
Table 1. Example of Transition Percentages from 𝒇 𝑵 𝒕
(from DS Image Figure 6)
𝑁𝑡 Frequency of 𝑁𝑡 𝑁𝑡
p
(%)
0 1 2.44
2 26 63.41
4 14 34.15
𝒇 𝑵 𝒕
= [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2,2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 2, 2, 2, 2, 2, 2, 0]
Figure 6. Example of result for 𝐟 𝐍 𝐭
after scanning process
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2.3. The First and Second Layer Classification
2.3.1. Designing the Filtering Table for First Layer Classification
The percentages of 𝑁𝑡 obtained when processing 100 frames from each of the five
classes are plotted in Figure 7. The X-axis represents 𝑁𝑡 whereas Y-axis represents 𝑁𝑡
p
, which
is the percentage of 𝑁𝑡. In our method, we use 10 pixels as the interval size for the scanning
process. From observation, a threshold can be set from the data to enable the classification for
the markers. At threshold value of 20% as shown as red dash line, a filtering table can be
designed as in Table 2. At least 2 types of markers can be classified, whereas SS and DS/SD
classes could produced the same result. To improve this problem, an additional percentage
value is introduced, which is 𝑁4
p
= 6, which is a multiplication of 𝑁2
p
= 2 and 𝑁3
p
= 4 and later
gain by 2 as formulated in (4) are plotted in Figures 7 (a)-(e) as inside the blue dash box. From
this additional percentage value, the filtering table can be improved as in Table 2 to classify
three road marker types namely D, DD and SS. As for SD and DS road marker type,
the algorithm will proceed to the second classifier as described in the next section.
𝑁4
p
= (𝑁2
p
× 𝑁3
p
) ∗ 2 (4)
(a) (b)
(c) (d)
(e)
Figure 1. Plotting Percentage of 𝑁𝑡 (%) for 5 different classes with 20% threshold line with,
𝑁4
p
= 6 included as inside the blue dash box (a) DD (b)D (c) DS (d) SD (e)SS
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Table 2. Filtering Table Construction from the Graph Observation Inclusive of 𝑁4
p
= 6
Condition for classifying 𝑁𝑡
p
> 20%
Binary transition 𝑁𝑡 0 2 4 6
Dashed (D) C1 Yes Yes/No No No
Double Solid (DD) C2 No No Yes Yes/No
Single (SS) C3 No Yes No No
Solid-Dashed (SD) C4 No Yes Yes/No Yes
Dashed-Solid (DS) C5 No Yes Yes/No Yes
2.3.2. Contours Slope (Second Layer Classifier)
In the second layer classification, the aim is to separate the DS and SD classes which
requires the process to detect the contours (long and short markers). A DS class is recognized
by the location of the shorter contour (of the dashed marker) below the longer contour (the solid
marker) and an SD class is recognized vice versa. In Figure 8, the longer contour is indicated by
two points, A and B, and its both ends. The shorter contour is indicated by point C, which is
the centroid of the shorter contour. Hence by knowing the location of C, whether it’s below or
above the longer contour, the class SD or DS can be identified.
(a) (b)
Figure 2. Three points to calculate the slope (a) DS class (b) SD class
The slope of 𝑚 𝐴𝐶 =
𝐴 𝑦− 𝐶 𝑦
𝐴 𝑥−𝐶 𝑥
, between A and C will be smaller than the slope of
𝑚 𝐴𝐵 =
𝐴 𝑦− 𝐵 𝑦
𝐴 𝑥−𝐵 𝑥
between A and B, if the shorter contour is below the longer contour which is
found in DS class. On the other hand, the slope of 𝑚 𝐴𝐶, will be larger than the slope, 𝑚 𝐴𝐶, if
the shorter contour is below the longer contour for SD class. If both 𝑚 𝐴𝐶 and 𝑚 𝐴𝐵 are multiplied
with( )( )xxxx BACA −− , then the result of the multiplication,
( )( ) ( )( )xxyyxxxxAC BACABACAm −−=−− , (5)
will still be smaller than the result of another multiplication for DS class.
( )( ) ( )( )xxyyxxxxAB CABABACAm −−=−− , (6)
If the difference between these two multiplications is denoted as , the following
equation yields
( )( ) ( )( )xxyyxxyy CABABACA −−−−−= , (7)
then the value of can be used as the feature to determine or segregate between SD and DS
classes. If the < 0, then the road marker is classified as DS class. If ,0 then the road
marker is classified as SD class. Since the classification is only carried out to identify between
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SD and DS for this second layer classification, the value of will not equivalent as zero as C
will never be located on the straight line between A and B.
2.3.3. Outliers and Improving the Classification Result
By observing the graphs in Figure 7, there are potential outliers generated due to
the values calculated near to the threshold values at 20% as indicated by the red circle in
Figures 7 (a), (c) and (e). Other than introducing 𝑁4
p
= 6, this possible false error can also be
resolved by using temporal values between the video images. The markers tend to be
the similarly labeled for a set of adjacent frames. By using this possibility, the previous
classification result would be stored and if there is a transition of a class or marker type, it will
confirm on the next ten (10) frames of sequence. This method also improved the issue related
to the vehicle blocking the road marker as there is no firm classification result when this occurs
and the result will hold the last classification result as reference. Hence the validation for
transition types of marker requires 10*0.033s=0.33s at the most.
3. Results and Analysis
The proposed two-layer classification methods are implemented to classify the road
markers from a number of video clips recorded when driving at different road scenarios with
different road markers. The speed of the car while these video clips were taken was between
70-80 km/hr. In order to run the proposed road marker classification model, the features from
the first three video clips given in Table 3 are extracted and analyzed to see the significant
patterns for the classification process and it contain all types of the road markers which are
aimed to be classified. Manual comparison is performed on the predicted road marker type of
the algorithm with the ground truth on every frame and the accuracy is calculated as in (8)
where the total frames classified correctly from the ground truth ∑ 𝐹𝑇𝑃 is divided with the total
frames, ∑ 𝐹𝑇. It can be observed that the accuracy achieved in the training videos are well
above 98% except clip number 3 at 93% which due to the faint lane markers detected in
the video. This is essential to ensure that the classification performance achieved when running
this model on the testing videos is as good as possible.
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
∑ 𝐹 𝑇𝑃
∑ 𝐹 𝑇
𝑋 100% (8)
Table 3. Various Clips and Accuracy
Clip Name Resolution FPS Type Frames Features Analysis Test Accuracy (%)
1 Vid24 1280x720 30 D-DD-SD 1840 x x 98.04%
2 Vid25 1280x720 30 DD-DS 1099 x x 100%
3 Vid23 1280x720 30 DD-S 357 x x 93.84%
4 Vid_1_Test 1280x720 30 DD-DS-D 2019 x 97.17%
5 Vid_2_Test 1280x720 30 DD-D 796 x 94.47%
6 Vid_3_Test 1280x720 30 D-SD-DD 1287 x 100%
7 Vid_4_Test 1280x720 30 DD-D-SD 1547 x 97.54%
8 Vid_5_Test 1920x1080 30 SS 763 x 100%
The proposed classification model was later tested with five video clips, which are
numbered from 4 to 8 in Table 3. In video clip Vid_2_Test, which contains 796 frames having
road markers of types DD and D, the recorded accuracy is 94.47%, which still higher than 90%
but stands as the lowest amongst all other accuracy values achieved. This is mainly due to
the quality of this video clip which is more blurry compared on the other clips. The marker
transition had shown minimum effect on the classification accuracy as the algorithm
successfully revalidated between the false and true transition and contributed on
the accucarcy calculation.
As for the confusion matrix as in Table 4, we can find out that most error is coming from
the DD class due to the thresholding process resulting the output of combined contour at the top
of the lanes which is very hard to distinguish its threshold value. Examples of the thresholded
images are as in Figure 9 as in the blue circle. In this circle area, the scanning process will
resulted this as 𝑁𝑡 = 2 instead of 𝑁𝑡 = 2 and the classification would lead to SD instead of DD.
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Figure 9. Thresholding output for DD
Table 4. Confusion Matrix Table
Predicted Class
Dashed Double Solid Single Solid-Dashed Dashed-Solid
Actual Class
C1 C2 C3 C4 C5
C1 1254 32 0 0 0 97.44%
C2 0 2476 0 128 0 94.83%
C3 0 0 763 0 0 100%
C4 0 0 0 1080 0 100%
C5 0 0 0 0 679 100%
4. Conclusion
The paper presented a new approach for lane marker classification. The ROI selection
is based on the camera position to capture the road image. The ROI is processed and
the features of the lane marking are extracted for the training purposes and later for
the classification process. This new algorithm was tested with the local new videos and
the ground truth. Experimental results have shown that the accuracy values achieved by
the proposed new method achieved more than 93%. The errors from the classification operation
are mainly due to the faint road marker which caused wrong pre-processing result, sudden
illumination changes and the difference in the dimension of the lane markers.
Acknowledgements
The authors would like to acknowledge the funding support received from Universiti
Teknikal Malaysia Melaka (UTeM) from PJP/2014/FKE(15D)/S01357–Road Lane Recognition
Using Vision and Artificial Intelligence for Auto Assist Driver Alert System.
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