Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions
An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid tr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/5/1079 |
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| author | Muhammad Awais Javeed Muhammad Arslan Ghaffar Muhammad Awais Ashraf Nimra Zubair Ahmed Sayed M. Metwally Elsayed M. Tag-Eldin Patrizia Bocchetta Muhammad Sufyan Javed Xingfang Jiang |
| author_facet | Muhammad Awais Javeed Muhammad Arslan Ghaffar Muhammad Awais Ashraf Nimra Zubair Ahmed Sayed M. Metwally Elsayed M. Tag-Eldin Patrizia Bocchetta Muhammad Sufyan Javed Xingfang Jiang |
| author_sort | Muhammad Awais Javeed |
| collection | DOAJ |
| description | An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems. |
| first_indexed | 2024-03-11T07:28:05Z |
| format | Article |
| id | doaj.art-dc46c684b9164a8fa82beb497b9aa397 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T07:28:05Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-dc46c684b9164a8fa82beb497b9aa3972023-11-17T07:31:24ZengMDPI AGElectronics2079-92922023-02-01125107910.3390/electronics12051079Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road ConditionsMuhammad Awais Javeed0Muhammad Arslan Ghaffar1Muhammad Awais Ashraf2Nimra Zubair3Ahmed Sayed M. Metwally4Elsayed M. Tag-Eldin5Patrizia Bocchetta6Muhammad Sufyan Javed7Xingfang Jiang8School of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Automobile, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaDepartment of Mathematics, College of Science, King Saud University, Riyadh 11451, Saudi ArabiaFaculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, EgyptDipartimento di Ingegneria dell’Innovazione, Università del Salento, Via Monteroni, 73100 Lecce, ItalySchool of Physical Science and Technology, Lanzhou University, Lanzhou 730000, ChinaSchool of Miccroelectronics and Control Engineering, Changzhou University, Changzhou 213164, ChinaAn Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems.https://www.mdpi.com/2079-9292/12/5/1079lane line detectionintelligent vehiclesSobel operatorGaussian blur filterleast-squares filter edge extractionCanny algorithm |
| spellingShingle | Muhammad Awais Javeed Muhammad Arslan Ghaffar Muhammad Awais Ashraf Nimra Zubair Ahmed Sayed M. Metwally Elsayed M. Tag-Eldin Patrizia Bocchetta Muhammad Sufyan Javed Xingfang Jiang Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions Electronics lane line detection intelligent vehicles Sobel operator Gaussian blur filter least-squares filter edge extraction Canny algorithm |
| title | Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions |
| title_full | Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions |
| title_fullStr | Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions |
| title_full_unstemmed | Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions |
| title_short | Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions |
| title_sort | lane line detection and object scene segmentation using otsu thresholding and the fast hough transform for intelligent vehicles in complex road conditions |
| topic | lane line detection intelligent vehicles Sobel operator Gaussian blur filter least-squares filter edge extraction Canny algorithm |
| url | https://www.mdpi.com/2079-9292/12/5/1079 |
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