Pavement pothole detection system based on deep learning and binocular vision
Due to strong vibrations and the impact on high-speed vehicles, road potholes may cause discomfort to passengers, damaging the durability of the suspension and the integrity of the cargo. Therefore, a road pothole detection system is proposed, which uses deep learning and binocular vision for precis...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-08-01
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| Series: | Journal of Traffic and Transportation Engineering (English ed. Online) |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095756425001126 |
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| author | Tian Guan Jianyuan Cai Yu Wang Wei Yang Xiaobo Chang Yi Han |
| author_facet | Tian Guan Jianyuan Cai Yu Wang Wei Yang Xiaobo Chang Yi Han |
| author_sort | Tian Guan |
| collection | DOAJ |
| description | Due to strong vibrations and the impact on high-speed vehicles, road potholes may cause discomfort to passengers, damaging the durability of the suspension and the integrity of the cargo. Therefore, a road pothole detection system is proposed, which uses deep learning and binocular vision for precise detection in the front. 6,848 road surface pothole condition recognition datasets were constructed using a vehicle mounted binocular camera. The group attention shuffle block (GASB) is designed to enhance the expression of channel and spatial feature information in road images, while improving the existing shuffling network (ShuffleNetv2). This establishes a ShuffleNetv2 (GASB-ShuffleNetv2) model based on mixed attention for recognizing the state of road potholes. The experimental results show that the model has better accuracy than the basic model and can effectively detect road potholes. In addition, we replaced the ordinary convolution in the CenterNet feature extraction network with pyramid convolution with multiple receptive fields, and designed a feature fusion module in the same network to fuse low-level and high-level features related to holes, thus establishing a PF-CenterNet that combines pyramid convolution with feature fusion to detect areas containing road potholes. A pothole distance estimation model based on binocular vision was established by analyzing the stereo ranging model and semi-global block matching algorithm. After parameter calibration, images were rectified and stereo-matched to generate a disparity map. This map was optimized using a weighted least squares filter to fill blank areas. The 3D coordinates are then calculated based on the disparity provided with distance information. Finally, vehicle experiments were conducted to verify the effectiveness of the algorithm in meeting detection requirements while considering long-range perception accuracy. The experimental results show that the system can meet the needs of unmanned vehicles, enabling them to perceive potholes in advance, thereby issuing timely warnings to drivers. |
| format | Article |
| id | doaj-art-85fb5793ec084dfb9f303667085634e8 |
| institution | Kabale University |
| issn | 2095-7564 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Traffic and Transportation Engineering (English ed. Online) |
| spelling | doaj-art-85fb5793ec084dfb9f303667085634e82025-08-20T03:36:26ZengKeAi Communications Co., Ltd.Journal of Traffic and Transportation Engineering (English ed. Online)2095-75642025-08-011241100112310.1016/j.jtte.2024.08.001Pavement pothole detection system based on deep learning and binocular visionTian Guan0Jianyuan Cai1Yu Wang2Wei Yang3Xiaobo Chang4Yi Han5School of Automobile, Chang'an University, Xi'an 710064, ChinaSchool of Automobile, Chang'an University, Xi'an 710064, ChinaSchool of Automobile, Chang'an University, Xi'an 710064, ChinaSchool of Automobile, Chang'an University, Xi'an 710064, China; Corresponding author.China National Heavy Duty Truck Group Co., Ltd., Jinan 250000, ChinaSchool of Automobile, Chang'an University, Xi'an 710064, ChinaDue to strong vibrations and the impact on high-speed vehicles, road potholes may cause discomfort to passengers, damaging the durability of the suspension and the integrity of the cargo. Therefore, a road pothole detection system is proposed, which uses deep learning and binocular vision for precise detection in the front. 6,848 road surface pothole condition recognition datasets were constructed using a vehicle mounted binocular camera. The group attention shuffle block (GASB) is designed to enhance the expression of channel and spatial feature information in road images, while improving the existing shuffling network (ShuffleNetv2). This establishes a ShuffleNetv2 (GASB-ShuffleNetv2) model based on mixed attention for recognizing the state of road potholes. The experimental results show that the model has better accuracy than the basic model and can effectively detect road potholes. In addition, we replaced the ordinary convolution in the CenterNet feature extraction network with pyramid convolution with multiple receptive fields, and designed a feature fusion module in the same network to fuse low-level and high-level features related to holes, thus establishing a PF-CenterNet that combines pyramid convolution with feature fusion to detect areas containing road potholes. A pothole distance estimation model based on binocular vision was established by analyzing the stereo ranging model and semi-global block matching algorithm. After parameter calibration, images were rectified and stereo-matched to generate a disparity map. This map was optimized using a weighted least squares filter to fill blank areas. The 3D coordinates are then calculated based on the disparity provided with distance information. Finally, vehicle experiments were conducted to verify the effectiveness of the algorithm in meeting detection requirements while considering long-range perception accuracy. The experimental results show that the system can meet the needs of unmanned vehicles, enabling them to perceive potholes in advance, thereby issuing timely warnings to drivers.http://www.sciencedirect.com/science/article/pii/S2095756425001126Image processingUnmanned vehiclesDeep learningPavement pothole detectionBinocular vision |
| spellingShingle | Tian Guan Jianyuan Cai Yu Wang Wei Yang Xiaobo Chang Yi Han Pavement pothole detection system based on deep learning and binocular vision Journal of Traffic and Transportation Engineering (English ed. Online) Image processing Unmanned vehicles Deep learning Pavement pothole detection Binocular vision |
| title | Pavement pothole detection system based on deep learning and binocular vision |
| title_full | Pavement pothole detection system based on deep learning and binocular vision |
| title_fullStr | Pavement pothole detection system based on deep learning and binocular vision |
| title_full_unstemmed | Pavement pothole detection system based on deep learning and binocular vision |
| title_short | Pavement pothole detection system based on deep learning and binocular vision |
| title_sort | pavement pothole detection system based on deep learning and binocular vision |
| topic | Image processing Unmanned vehicles Deep learning Pavement pothole detection Binocular vision |
| url | http://www.sciencedirect.com/science/article/pii/S2095756425001126 |
| work_keys_str_mv | AT tianguan pavementpotholedetectionsystembasedondeeplearningandbinocularvision AT jianyuancai pavementpotholedetectionsystembasedondeeplearningandbinocularvision AT yuwang pavementpotholedetectionsystembasedondeeplearningandbinocularvision AT weiyang pavementpotholedetectionsystembasedondeeplearningandbinocularvision AT xiaobochang pavementpotholedetectionsystembasedondeeplearningandbinocularvision AT yihan pavementpotholedetectionsystembasedondeeplearningandbinocularvision |