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.
A Method for Predicting Vehicles Motion Based on Road Scene Reconstruction an...ITIIIndustries
The suggested method helps predicting vehicles movement in order to give the driver more time to react and avoid collisions on roads. The algorithm is dynamically modelling the road scene around the vehicle based on the data from the onboard camera. All moving objects are monitored and represented by the dynamic model on a 2D map. After analyzing every object’s movement, the algorithm predicts its possible behavior.
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.
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.
A Method for Predicting Vehicles Motion Based on Road Scene Reconstruction an...ITIIIndustries
The suggested method helps predicting vehicles movement in order to give the driver more time to react and avoid collisions on roads. The algorithm is dynamically modelling the road scene around the vehicle based on the data from the onboard camera. All moving objects are monitored and represented by the dynamic model on a 2D map. After analyzing every object’s movement, the algorithm predicts its possible behavior.
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.
Traffic Light Detection for Red Light Violation Systemijtsrd
The goal of Red Light Violation Detection System RLVDS is to track down vehicles that violated traffic regulations using surveillance cameras and image processing techniques. A complete automated red light runner detection algorithm that satisfies real time requirement and gives higher accuracy have been developed. The system detects simultaneously a traffic light sequence, stop line and detection region to detect moving vehicles and capture the vehicles beyond the stop line while the light is red and finally generates the red light running vehicle images with date and time information. In this paper, we propose a traffic light detection algorithm which is one of the processes of automated red light runner detection system. The proposed traffic light detection system includes four steps color space conversion based on RGB color space, some regions are extracted as candidates of a traffic light and morphological operation is applied to eliminate disturbance in the environment that can interfere with the traffic light states. Then, the types of traffic signals are judged by calculating image moments and the results of experiment show that the system is stable and reliable. Thwe War War Zaw | Ohnmar Win ""Traffic Light Detection for Red Light Violation System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25185.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25185/traffic-light-detection-for-red-light-violation-system/thwe-war-war-zaw
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.
"Detecting road lane is one of the key processes in vision based driving assistance system and autonomous vehicle system. The main purpose of the lane detection process is to estimate car position relative to the lane so that it can provide a warning to the driver if the car starts departing the lane. This process is useful not only to enhance safe driving but also in self driving car system. A novel approach to lane detection method using image processing techniques is presented in this research. The method minimizes the complexity of computation by the use of prior knowledge of color, intensity and the shape of the lane marks. By using prior knowledge, the detection process requires only two different analyses which are pixel intensity analysis and color component analysis. The method starts with searching a strong pair of edges along the horizontal line of road image. Once the strong edge is detected the process continues with color analysis on pixels that lie between the edges to check whether the pixels belong to a lane or not. The process is repeated for different positions of horizontal lines covering the road image. The method was successfully tested on selected 20 road images collected from internet. Ery M. Rizaldy | J. M. Nursherida | Abdul Rahim Sadiq Batcha ""Reduced Dimension Lane Detection Method"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19136.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/19136/reduced-dimension-lane-detection-method/ery-m-rizaldy"
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.
Road markers classification using binary scanning and slope contoursTELKOMNIKA JOURNAL
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.
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,
A computer vision-based lane detection technique using gradient threshold and...IJECEIAES
Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.
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.
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.
Statistics indicate that most road accidents occur due to a lack of time to react to instant traffic. This problem can be addressed with self-driving vehicles with the application of automated systems to detect such traffic events. The Autonomous Vehicle Navigation System (ATS) has been a standard in the Intelligent Transport System (ITS) and many Driver Assistance Systems (DAS) have been adopted to support these Advanced Autonomous Vehicles (IAVs). To develop these recognition systems for automated self-driving cars, it's important to monitor and operate in real-time traffic events. It requires the correct detection and response of traffic event an automated vehicle. In this paper proposed to develop such a system by applying image recognition to detect and respond to a road blocker by means of real-time distance measurement. To study the performance by measuring accuracy and precision of road blocker detection system and distance calculation, various experiments were conducted by using Shalom frame dataset and detection accuracy, precision of 99%, 100%, while distance calculation 97%, 99% has been achieved by this approach.
A 3D reconstruction-based method using unmanned aerial vehicles for the repre...IJECEIAES
Due to the fast growth of cities worldwide, roads are increasing daily, and pavement maintenance has become very heavy and costly. Despite all efforts made under the pavement management system to keep the road surface in good shape, several road sections need to be in better condition, which presents a danger for drivers and pedestrians. This paper proposes a novel pavement 3D reconstruction and segmentation approach using the structure from motion technique, unmanned aerial vehicle, and digital camera. The method consists of the 3D modeling of the road by using images taken from different perspectives and the structure from motion technique. In this method, points cloud is sampled and cleaned using statistical outlier removal and noise filters. After that, duplicated and isolated points are eliminated to retain only significant data. The normal road plane is estimated using the principal component analysis technique and the remaining points. This plan presents a root mean square less than 0.85 cm. Finally, distances from those points to the normal plane are calculated and clustered to segment the road into distressed and non-distressed areas. The proposed approach presents a similarity rate to the survey measurement passed 95%. It has demonstrated promising results and has the potential for further improvement by optimizing various steps.
REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...ijcsit
Lane detection and tracking is one of the key features of advanced driver assistance system. Lane detection is finding the white markings on a dark road. Lane tracking use the previously detected lane markers and adjusts itself according to the motion model. In this paper, review of lane detection and tracking algorithms developed in the last decade is discussed. Several modalities are considered for lane detection which
include vision, LIDAR, vehicle odometry information,information from global positioning system and digital maps. The lane detection and tracking is one of the challenging problems in computer vision.Different vision based lane detection techniques are explained in the paper. The performance of different lane detection and tracking algorithms is also compared and studied.
Lane and Object Detection for Autonomous Vehicle using Advanced Computer VisionYogeshIJTSRD
The vision of this project is to develop lane and object detection in Autonomous Vehicle system to run efficiently in normal road condition and to eliminate the use of high cost Light based LiDAR system to implement high resolution cameras with advanced computer vision and deep learning technology to provide an Advanced Driver Assistance System ADAS . Detecting lane lines could be a crucial task for any self driving autonomous vehicle. Hence, this project was focused to identify lane lines on the road using OpenCV. The OpenCV tools such as colour selection, the region of interest selection, grey scaling, canny edge detection and perspective transformation are being employed. This project is modelled as an integration of two systems to solve the real time implementation problem in autonomous vehicles. The first part of the system is lane detection by advanced computer vision techniques to detect the lane lines to command the vehicle to stay inside the lane marking. The second part of the system is object detection and tracking is to detect and track the vehicle and pedestrians on the road to get a clear understanding of the environment to plan and generate a trajectory to navigate the autonomous vehicle safely to its destination without any crashes, this is done by a special deep learning method called transfer learning with Single Shot multibox Detection SSD algorithm and Mobile Net architecture. G. Monika | S. Bhavani | L. Azim Jahan Siana | N. Meenakshi "Lane and Object Detection for Autonomous Vehicle using Advanced Computer Vision" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39952.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39952/lane-and-object-detection-for-autonomous-vehicle-using-advanced-computer-vision/g-monika
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
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Traffic Light Detection for Red Light Violation Systemijtsrd
The goal of Red Light Violation Detection System RLVDS is to track down vehicles that violated traffic regulations using surveillance cameras and image processing techniques. A complete automated red light runner detection algorithm that satisfies real time requirement and gives higher accuracy have been developed. The system detects simultaneously a traffic light sequence, stop line and detection region to detect moving vehicles and capture the vehicles beyond the stop line while the light is red and finally generates the red light running vehicle images with date and time information. In this paper, we propose a traffic light detection algorithm which is one of the processes of automated red light runner detection system. The proposed traffic light detection system includes four steps color space conversion based on RGB color space, some regions are extracted as candidates of a traffic light and morphological operation is applied to eliminate disturbance in the environment that can interfere with the traffic light states. Then, the types of traffic signals are judged by calculating image moments and the results of experiment show that the system is stable and reliable. Thwe War War Zaw | Ohnmar Win ""Traffic Light Detection for Red Light Violation System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25185.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25185/traffic-light-detection-for-red-light-violation-system/thwe-war-war-zaw
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.
"Detecting road lane is one of the key processes in vision based driving assistance system and autonomous vehicle system. The main purpose of the lane detection process is to estimate car position relative to the lane so that it can provide a warning to the driver if the car starts departing the lane. This process is useful not only to enhance safe driving but also in self driving car system. A novel approach to lane detection method using image processing techniques is presented in this research. The method minimizes the complexity of computation by the use of prior knowledge of color, intensity and the shape of the lane marks. By using prior knowledge, the detection process requires only two different analyses which are pixel intensity analysis and color component analysis. The method starts with searching a strong pair of edges along the horizontal line of road image. Once the strong edge is detected the process continues with color analysis on pixels that lie between the edges to check whether the pixels belong to a lane or not. The process is repeated for different positions of horizontal lines covering the road image. The method was successfully tested on selected 20 road images collected from internet. Ery M. Rizaldy | J. M. Nursherida | Abdul Rahim Sadiq Batcha ""Reduced Dimension Lane Detection Method"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19136.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/19136/reduced-dimension-lane-detection-method/ery-m-rizaldy"
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.
Road markers classification using binary scanning and slope contoursTELKOMNIKA JOURNAL
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.
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,
A computer vision-based lane detection technique using gradient threshold and...IJECEIAES
Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.
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.
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.
Statistics indicate that most road accidents occur due to a lack of time to react to instant traffic. This problem can be addressed with self-driving vehicles with the application of automated systems to detect such traffic events. The Autonomous Vehicle Navigation System (ATS) has been a standard in the Intelligent Transport System (ITS) and many Driver Assistance Systems (DAS) have been adopted to support these Advanced Autonomous Vehicles (IAVs). To develop these recognition systems for automated self-driving cars, it's important to monitor and operate in real-time traffic events. It requires the correct detection and response of traffic event an automated vehicle. In this paper proposed to develop such a system by applying image recognition to detect and respond to a road blocker by means of real-time distance measurement. To study the performance by measuring accuracy and precision of road blocker detection system and distance calculation, various experiments were conducted by using Shalom frame dataset and detection accuracy, precision of 99%, 100%, while distance calculation 97%, 99% has been achieved by this approach.
A 3D reconstruction-based method using unmanned aerial vehicles for the repre...IJECEIAES
Due to the fast growth of cities worldwide, roads are increasing daily, and pavement maintenance has become very heavy and costly. Despite all efforts made under the pavement management system to keep the road surface in good shape, several road sections need to be in better condition, which presents a danger for drivers and pedestrians. This paper proposes a novel pavement 3D reconstruction and segmentation approach using the structure from motion technique, unmanned aerial vehicle, and digital camera. The method consists of the 3D modeling of the road by using images taken from different perspectives and the structure from motion technique. In this method, points cloud is sampled and cleaned using statistical outlier removal and noise filters. After that, duplicated and isolated points are eliminated to retain only significant data. The normal road plane is estimated using the principal component analysis technique and the remaining points. This plan presents a root mean square less than 0.85 cm. Finally, distances from those points to the normal plane are calculated and clustered to segment the road into distressed and non-distressed areas. The proposed approach presents a similarity rate to the survey measurement passed 95%. It has demonstrated promising results and has the potential for further improvement by optimizing various steps.
REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...ijcsit
Lane detection and tracking is one of the key features of advanced driver assistance system. Lane detection is finding the white markings on a dark road. Lane tracking use the previously detected lane markers and adjusts itself according to the motion model. In this paper, review of lane detection and tracking algorithms developed in the last decade is discussed. Several modalities are considered for lane detection which
include vision, LIDAR, vehicle odometry information,information from global positioning system and digital maps. The lane detection and tracking is one of the challenging problems in computer vision.Different vision based lane detection techniques are explained in the paper. The performance of different lane detection and tracking algorithms is also compared and studied.
Lane and Object Detection for Autonomous Vehicle using Advanced Computer VisionYogeshIJTSRD
The vision of this project is to develop lane and object detection in Autonomous Vehicle system to run efficiently in normal road condition and to eliminate the use of high cost Light based LiDAR system to implement high resolution cameras with advanced computer vision and deep learning technology to provide an Advanced Driver Assistance System ADAS . Detecting lane lines could be a crucial task for any self driving autonomous vehicle. Hence, this project was focused to identify lane lines on the road using OpenCV. The OpenCV tools such as colour selection, the region of interest selection, grey scaling, canny edge detection and perspective transformation are being employed. This project is modelled as an integration of two systems to solve the real time implementation problem in autonomous vehicles. The first part of the system is lane detection by advanced computer vision techniques to detect the lane lines to command the vehicle to stay inside the lane marking. The second part of the system is object detection and tracking is to detect and track the vehicle and pedestrians on the road to get a clear understanding of the environment to plan and generate a trajectory to navigate the autonomous vehicle safely to its destination without any crashes, this is done by a special deep learning method called transfer learning with Single Shot multibox Detection SSD algorithm and Mobile Net architecture. G. Monika | S. Bhavani | L. Azim Jahan Siana | N. Meenakshi "Lane and Object Detection for Autonomous Vehicle using Advanced Computer Vision" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39952.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39952/lane-and-object-detection-for-autonomous-vehicle-using-advanced-computer-vision/g-monika
Similar to A computer vision-based lane detection approach for an autonomous vehicle using the image hough transformation and the edge features (20)
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Cosmetic shop management system project report.pdf
A computer vision-based lane detection approach for an autonomous vehicle using the image hough transformation and the edge features
1. See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/363468475
A computer vision-based lane detection approach for an autonomous vehicle
using the image hough transformation and the edge features
Preprint · September 2022
DOI: 10.13140/RG.2.2.14924.69768
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2. A computer vision-based lane detection
approach for an autonomous vehicle using the
image hough transformation and the edge
features ⋆
Md. Abdullah Al Noman1,2 ID
, Md. Faishal Rahaman2 ID
, Zhai Li∗1,2 ID
,
Samrat Ray3 ID
, and Chengping Wang1,2 ID
1
National Engineering Laboratory for Electric Vehicles, Beijing Institute of
Technology, Beijing, 100811, China.
2
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100811,
China.
3
Sunstone Calcutta Institute of Engineering and Management, Kolkata, India.
Abstract. Lane detection systems play a critical role in ensuring safe
and secure driving by alerting the driver of lane departures. Lane de-
tection may also save passengers’ lives if they go off the road owing to
driver distraction. The article presents a three-step approach for detect-
ing 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 pic-
ture. 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 mil-
liseconds 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.
Keywords: Lane Detection · Hough Transformation · Edge Detection ·
Autonomous Vehicles · Computer Vision.
1 Introduction
Each year, many accidents occur worldwide as a result of the expanding number
of automobiles, resulting in significant financial and human losses [1]. Human
⋆
Corresponding Author: Dr. Zhai Li, National Engineering Laboratory for
Electric Vehicles, Beijing Institute of Technology, Beijing, China . Contact:
Zhaili26@bit.edu.cn
3. 2 M.A.A.Noman et al.
error is the primary cause of many accidents, including weariness, sleepiness, lack
of focus, and ignorance of road conditions [1]. Automobile manufacturers have
made strenuous attempts in recent years to include driver assistance technologies
into their vehicles to aid drivers in controlling and steering the vehicle [2]. Driver
assistance technologies are becoming more prevalent as a means of boosting the
security of premium automobiles [3]. The Fig. 1 shows the perception sensor
location and corresponding ADAS function support for autonomous vehicles.
Fig. 1. Perception sensor location and corresponding ADAS function support for au-
tonomous vehicles.
Some of the driver assistance systems include road diversion warning systems,
accident warning systems, lane departure detection systems, junction and traffic
light detection systems, and objects detection systems in front of the vehicle [4].
Among the planned driver aid systems, the lane detection system is critical for
avoiding vehicle deviations and road accidents. Smart vehicles [2]are automobiles
equipped with driving aid technology. The importance of road line detection in
the realm of intelligent vehicles is substantial, since it has several applications
in autonomous vehicles [2]. Intelligent automobiles receive data from road lines
and direct the vehicle between them [3].
In recent years along with Autonomous Vehicles, computer vision has been
introduced in several places, such as fraud detection [5], forensic system [6],
home security systems [7], accident findings [8], IoT devices [9] and so on.The
lane detection refers to the process of determining the limits of the lines in a
picture of a road without knowledge of the road lines’ locations [2]. The lane
detecting system is meant to provide a prompt reaction and warning to the
driver in the case of a diversion from the primary path, allowing for increased
vehicle control. Lane detection has been touted as a critical component of safe
driving technology. By accurately recognizing the car’s location and traveling
within the road lines, this technology prevents the vehicle from diverting.
There are various methods for obtaining road lines in lane detecting sys-
tems. These include the placement of magnetic indications on highways, as well
as the use of sensors, high-precision GPS, and image processing [10]. The most
precise method of determining road lines is to place magnetic markers through-
out the road that are detectable by automobile sensors [10]. Another method
4. Title Suppressed Due to Excessive Length 3
of determining a vehicle’s present location is to calculate the worldwide change
in position relative to road lines [10], which is also somewhat costly. The most
accessible and cost-effective method of identifying road lines is by image process-
ing, which extracts road lines from an image taken with a video camera [10]. Due
to the fact that video pictures provide useful information about their surround-
ings [3], they are critical for lane detection. In-car cameras mounted behind the
windshield are utilized in lane detecting systems.
For instance, Bertozzi (1998) used the GOLD system, one of the most widely
used lane detecting systems, which is based on road imagery through a camera
mounted on a vehicle [3]. The path is determined using a method called model
matching. Additionally, the car’s front end is located via stereo image processing.
The perspective impact of the picture is erased in this manner, allowing for the
application of the pattern matching methodology [11]. The image’s straight lines
are then retrieved by employing a horizontal edge detector to look for horizontal
patterns of dark-light-dark illumination. Then, pieces that are adjacent or have
the same direction are blended to reduce the likelihood of inaccuracy due to
noise or obstruction [12]. Finally, lines that correspond to a certain road model
are chosen.
This experiment presented a computer vision-based approach for effectively
distinguishing lanes in any surrounding environment. The first stage is neces-
sary preprocessing, such as noise reduction, RGB to grey-scale conversion, and
binarizing the input image. The region of interest will be selected as a polygon
region to speed up the processing speed in front of the vehicle. Finally, applying
the edge detection approach will help acquire the image’s edges in the region
of interest and the Hough transform to identify lanes on the input image. The
remainder of the essay is divided into the following sections; we have discussed
current existing similar algorithms in Section 2, and the method of the suggested
lane-detecting approach is shown and explained in Section 3. The experimental
findings and discussion are shown in Section 4 using input and output data. The
paper comes to a conclusion with Section 5.
2 Related Works
In paper [13] authors apply the Hough transform and Euclidean distance in their
investigation. To begin, the picture is converted from RGB to YCbCr space.
Since the human visual system is more sensitive to light, the Y component of
the picture is employed to identify the lines. To increase the system’s speed and
accuracy, the bottom portion of the picture is chosen as the region of interest.
Equalization of histograms is employed to improve the contrast between the road
surface and the road lines in the region of interest, and an Otsu thresholding
binary picture is created [13]. The area of interest is then split into two sub-areas,
with the Canny edge detection and Hough transform techniques employed to
independently identify the left and right lines of the road for each sub-region [13].
The author of [14] provides a technique for recognizing road lines that are ro-
bust to changes in illumination. This approach begins by extracting the image’s
5. 4 M.A.A.Noman et al.
edges using Canny edge detection, then obtaining the image’s lines using the
Hough transform and calculating the collision position of the discovered lines.
The vanishing point is chosen to be the centre of the district with the most votes,
and the region of interest is chosen to be the bottom area of the vanishing point
in the picture. To ensure that the suggested method is robust to fluctuations in
brightness, the road’s yellow and white lines are calculated independently. The
binary picture is then formed by assigning the binary value 1 to the regions next
to the road lines and 0 to the remaining image areas. The areas of the lines are
then noted, and the centres of each area are determined in the binary picture
using the linked component clustering algorithm. Additionally, each region’s an-
gle and point of contact with the y-axis are determined. Areas with the same
angle and junction are joined to make an area, and the picture denotes the left,
and right lines of the road [14].
According to [15], the input picture is first taken out of perspective to align
the road lines [15]. The colour picture is then transformed to greyscale, and
the grey image’s edges are retrieved using an edge recognition algorithm [9].
The morphological expansion operator is used to eliminate small picture noises.
Then, using the Hough transform, the road lines are recognized and compared
to the previous picture frame lines. Lines are maintained if they fit; otherwise,
the following Hough transform lines are examined.
To ease the process of identifying lines, [16] has used a bird’s eye perspective
to capture the road picture in front of the automobile, which makes the road
lines parallel. To identify the picture’s edges, two one-dimensional Gaussian and
Laplace filters are utilized [16], and the binary image is generated using the
Otsu threshold and computed using the Hough transform of the image lines.
Following that, the major road lines are created using a series of horizontal lines
and their intersections with the picture lines are detected using the least mean
square method [16].
The research [17] reported a robust Hough domain lane detecting system. The
proposed approach employs road pictures to statistically calculate the predicted
location and deviation of the road width. Additionally, a mask is built in the
Hough domain to constrain the road width, and vehicle location as required [17].
The authors of [18] provided a novel strategy for preprocessing and identifying
the region of interest in their research. The input frame is read first, and then the
input picture is preprocessed; the area of interest is then picked, and the road
lines are detected. Pre-processing involves converting the picture to HSV space
and extracting the image’s white characteristics. The picture is then smoothed,
and the noise effect is reduced using a Gaussian filter. Following that, the picture
is transformed to binary using threshold processing. The region of interest is then
determined to be the bottom half of the picture. The edge detection technique
is used to extract the image’s edges, and the Hough transform is used to identify
the road’s major lines. Finally, real-time detection and tracking of road lines are
accomplished using the extended Kalman filter [18].
In [19] researchers developed a unique technique for lane recognition with
a high degree of accuracy. The three-layer input picture is reduced to a single
6. Title Suppressed Due to Excessive Length 5
layer, and the sharpness is increased. The zone of interest in this research is
determined by the lowest safe distance from the vehicle ahead. To provide a
smooth lane fitting, the Hough Transform is utilized.
The paper [20] presents a multi-stage Hough space computation for a lane
detecting challenge. A fast Hough Transform was devised to extract and cat-
egorize line segments from pictures. Kalman filtering was used to smooth the
probabilistic Hough space between frames in order to minimize noise caused by
occlusion, vehicle movement, and classification mistakes. The filtered probabilis-
tic Hough space was used to filter out line segments with low probability values
(threshold set to 0.7) and retain those with high probability values.
The paper [21] defined a Region of Interest (ROI) to ensure a dependable
real-time system. The Hough Transform is used to extract boundary information
and to identify the margins of road lane markers. Otsu’s threshold is used to
improve preprocessing results, compensate for illumination issues, and to gather
gradient information. The suggested technique enables precise lane monitoring
and gathers reliable vehicle orientation data in [21,22] also mentioned this data
in their research.
3 Methodology
This article uses video pictures obtained from the road by a camera positioned in-
side the automobile to determine road lines in real time. The camera is mounted
inside the vehicle behind the windscreen and almost in the center to offer a view
of the road.
The suggested approach for determining road lines involves three steps: pre-
processing, area selection, and road line determination. Pre-processing steps in-
clude noise reduction, RGB to greyscale conversion, and input picture binariza-
tion. Then, a polygonal region in front of the automobile is picked as the area of
interest due to the presence of road lines in that area. The third stage uses the
Canny edge detection technique to identify the image’s edges inside the region
of interest, and the Hough transform to identify the road’s major lines. Fig. 2
depicts the block diagram of the proposed technique.
3.1 Preprocessing
The noise in the picture acquired by the camera positioned inside the automobile
is eliminated at this step using a Gaussian filter [23]. The output of Fig. 5 shows
the smooth image after the Gaussian filter [23] have applied in the input image
Fig. 3.
To minimize algorithm computations, the input picture is transformed to
greyscale from RGB color space. In grey mode, Fig. 5 depicts the input picture.
Then, using the Dong method [22], the input picture is converted from grey
space Fig. 5 to binary space Fig. 6.
7. 6 M.A.A.Noman et al.
Fig. 2. Simplified block diagram of proposed technique.
3.2 Region of Interest
The road lines are visible in front of and on both sides of the automobile in the
photos received from the road surface. The region of interest in this article is
a dynamic polygonal section of the picture in front of the automobile that was
chosen using a trapezoidal mask. The information about the image’s vanishing
point is utilized to construct the trapezoidal mask. A trapezoid is formed in
the bottom portion of the vanishing point that encompasses the region directly
in front of the automobile. The Fig. 7 illustrates a mask created for the input
picture sample in which the portion of the image corresponding to the region of
interest is one, and the remainder is zero. By applying this mask to the input
binary picture, the preferred region is picked, containing the road lines just in
front of the automobile. The output binary picture in Fig. 8 results from applying
the designed mask to the input binary image.
8. Title Suppressed Due to Excessive Length 7
Fig. 3. An example of the input image.
Fig. 4. Input image after noise removing.
3.3 Determination of Road Lines
The usage of picture edges is a very helpful and effective feature for detecting
things in photographs. The image’s edge is the point at which the brightness
abruptly shifts. As long as the quantity of light on the road is consistent and
differs from the brightness of the lines, the border between the lines and the road
(edge) may be determined. Numerous algorithms are available in this sector,
including those developed by Sobel, Canny, Roberts, and Prewitt. The Canny
edge detection technique is used in this article [24]. Six steps are required to
identify the edge using the Canny edge detection technique. The first step is to
filter out the noise in the original picture, which is accomplished using a Gaussian
filter [23] and a basic mask. The second stage is to locate strong edges at any
location by using the gradient amplitude. Two masks are applied to the picture
for this purpose, the gradient amplitude in the x and y directions is computed,
and the edge strength is determined using (1).
G = Gx + Gy (1)
where G represents the edge strength, Gx represents the gradient amplitude
in the x direction, and Gy represents the gradient amplitude in the y direction
in (2).
Gx =
−1 0 +1
−2 0 +2
−1 0 +1
, Gy =
+1 +2 +1
0 0 0
−1 −2 −1
(2)
9. 8 M.A.A.Noman et al.
Fig. 5. Input image converted into gray image.
Fig. 6. Gray image converted into a binary image.
The third stage establishes the orientation of the picture edges generated via
the use of Canny masks (3).
θ = arctan(
Gy
Gx
) (3)
Where θ denotes the edge direction, Gx denotes the gradient along the x axis,
and Gy denotes the gradient along the y axis.
The fourth step matches the directions determined in the previous step to
one of four angles of 0, 45, 90, or 135 degrees. The fifth step is to suppress non-
maximum edges by checking the direction and removing those that cannot be
recognized. In the sixth stage, the Canny algorithm is utilized with the hysteresis
approach, which defines two higher and lower threshold values. Any pixel with
a gradient larger than or equal to the lower threshold is allowed, whereas any
pixel with a gradient less than or equal to the lower threshold is rejected. We
extract the picture’s edges by using the Canny edge detection algorithm [24] to
the binary image. The outcome of applying the edge finder algorithm on the
previous stage picture is seen in Fig. 9.
The Hough transform is used to identify the major lines of the road in the
following. Hough transform is a method for extracting features from digital im-
ages that is used in digital image processing, machine vision, and image analysis.
The objective of this conversion is to discover the many forms included inside
the picture. Initially, the Hough transform was used to recognize visual lines,
but it was later extended to identify diverse forms such as circles and ovals. The
Hough transform is employed in this technique to determine and indicate the
10. Title Suppressed Due to Excessive Length 9
Fig. 7. An example of the input image Mask to determine the region of interest.
Fig. 8. Binary image after applying the ROI mask.
Fig. 9. Apply the Canny algorithm into the binary image.
locations of road lines. Then, as illustrated in Fig. 10, the nearest lines on both
sides to the automobile are deemed road lines on the left and right.
4 Results and Discussions
The IROADS database [25, 26] is used to evaluate the performance of the pro-
posed algorithm. The database consists of 7 collections with a total of 4700
image strings. It covers almost all possible road conditions, including daylight,
night darkness, excessive sunlight, tunnel light, day and night rainfall, and snowy
roads. The dimension of each frame in this database is 360x640. Given that this
paper examines the different lighting conditions per day, four sets of data from
the IROADS database, including daytime driving in sunny, rainy, snowy, and
in-tunnel conditions, are reviewed. The average detection rate for 2828 frames
11. 10 M.A.A.Noman et al.
Fig. 10. Simplified output of the input image.
from the IROADS database in various daylight circumstances is 96.78 percent,
as shown in Table 1.
Table 1. Accuracy of the proposed method under different weather conditions.
IROAD database Total number
of frames
Number of in-
correct detection
frames
Identification
rate(%)
Number of
Frames Per
Second
IROAD Daylight 903 32 96.40 35.2
IROAD Rainy Day 1049 7 99.38 35.6
IROAD Snow Day 569 31 94.59 36.2
IROAD Tunnel 307 21 93.12 35.8
Total 2828 91 96.78 35.7
The Fig. 11 illustrates the results of the proposed algorithm’s road line
identification in various driving circumstances throughout the day.
The suggested method produces the following two modes of operation on
each frame of the input picture.
1. Accurate detection, in which the lines on both sides of the vehicle are ap-
propriately detected.
2. Incorrect detection, in which the lines on both sides of the automobile or
on just one side are not accurately recognised. The suggested algorithm’s
recognition rate is determined on the labels created before utilizing
Detection Rate =
Number of identified frames
Total number of frames
× 100 (4)
The suggested approach is implemented in Python on a laptop with a 2.40
GHz CPU and 8 GB RAM, and it takes an average of 28 milliseconds to analyze
each frame. As a result, the suggested method accurately recognizes road lines
in a timely manner. To evaluate the suggested algorithm’s performance, it was
compared to many methods already published. Table 2 compares the findings
obtained using Kortli’s approach [14], Suto’s method [27], Tian’s method [28],
and this study’s method using the IROADS dataset [26]. According to the find-
ings of this experiment, the suggested technique outperforms the current method
12. Title Suppressed Due to Excessive Length 11
Fig. 11. Output of proposed technique in different environmental scenarios.
in every category except the Tunnel scenario. This is mostly due to the fact that
the line indicators are muted in this case owing to the tunnel lights and hence
are not recognized for the suggested technique. However, if earlier frames’ data
is utilised, the outcomes may be enhanced.
Table 2. Comparative result analyses of the proposed method with some existing
techniques
Methods IROAD
Day Light
IROAD
Dainy Day
IROAD
Snow Day
IROAD
Tunnel
Total
Kortli’s Method [14] 95.79 88.27 86.64 93.51 90.81
Suto’s Method [27] 96.12 93.42 92.44 94.14 94.17
Tian’s Method [28] 96.23 94.75 93.67 94.46 94.99
The proposed
Method
96.40 99.38 94.59 93.12 96.78
The findings demonstrate the suggested algorithm’s usefulness in identifying
lanes under a variety of scenarios. The suggested approach does not yet include
tracking, since lanes are detected individually in each picture without the use
of temporal information. Although the suggested method for recognizing road
lines works well in 97% of situations, it may fail in 3% of circumstances due to
road lines fading, side vehicle covering of road lines, and driver direction shift.
5 Conclusions
Lane detection refers to the process of determining the position of lane borders
in a road picture without having previous information about their location. Lane
13. 12 M.A.A.Noman et al.
detection is intended to notify the driver about departures from the main lane,
allowing for improved vehicle control on the road. The purpose of this article is to
provide a simple approach for identifying road lines on high-speed video pictures
by using the edge feature. The suggested technique begins with the required pre-
processing, which includes noise reduction, picture conversion to greyscale, and
finally binary mode. The ideal region was then found to be a polygonal area just
in front of the vehicle. Finally, the Canny edge detection technique and Hough
transform were used to identify road lines.
The suggested algorithm’s performance was evaluated using the IROADS
database, with each frame having a size of 360x640. The research examines four
datasets from the IROADS database, including daylight driving in sunny, wet,
and snowy circumstances, as well as indoor tunnels. The suggested technique
executes on average in 28 milliseconds per frame and achieves a detection rate
of 96.78 per cent.
References
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based driver assistance systems: Survey, taxonomy and advances. In 2015 IEEE
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2039. IEEE, 2015.
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