Vehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX Network
To address the problems that exist in the target detection of vehicle-mounted visual sensors in foggy environments, a vehicle target detection method based on an improved YOLOX network is proposed. Firstly, to address the issue of vehicle target feature loss in foggy traffic scene images, specific c...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/194 |
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| author | Zhaohui Liu Huiru Zhang Lifei Lin |
| author_facet | Zhaohui Liu Huiru Zhang Lifei Lin |
| author_sort | Zhaohui Liu |
| collection | DOAJ |
| description | To address the problems that exist in the target detection of vehicle-mounted visual sensors in foggy environments, a vehicle target detection method based on an improved YOLOX network is proposed. Firstly, to address the issue of vehicle target feature loss in foggy traffic scene images, specific characteristics of fog-affected imagery are integrated into the network training process. This not only augments the training data but also improves the robustness of the network in foggy environments. Secondly, the YOLOX network is optimized by adding attention mechanisms and an image enhancement module to improve feature extraction and training. Additionally, by combining this with the characteristics of foggy environment images, the loss function is optimized to further improve the target detection performance of the network in foggy environments. Finally, transfer learning is applied during the training process, which not only accelerates network convergence and shortens the training time but also further improves the robustness of the network in different environments. Compared with YOLOv5, YOLOv7, and Faster R-CNN networks, the mAP of the improved network increased by 13.57%, 10.3%, and 9.74%, respectively. The results of the comparative experiments from different aspects illustrated that the proposed method significantly enhances the detection performance for vehicle targets in foggy environments. |
| format | Article |
| id | doaj-art-38bd2ede2c174a7ab1149de07c137330 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-38bd2ede2c174a7ab1149de07c1373302025-01-10T13:21:12ZengMDPI AGSensors1424-82202025-01-0125119410.3390/s25010194Vehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX NetworkZhaohui Liu0Huiru Zhang1Lifei Lin2College of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaTo address the problems that exist in the target detection of vehicle-mounted visual sensors in foggy environments, a vehicle target detection method based on an improved YOLOX network is proposed. Firstly, to address the issue of vehicle target feature loss in foggy traffic scene images, specific characteristics of fog-affected imagery are integrated into the network training process. This not only augments the training data but also improves the robustness of the network in foggy environments. Secondly, the YOLOX network is optimized by adding attention mechanisms and an image enhancement module to improve feature extraction and training. Additionally, by combining this with the characteristics of foggy environment images, the loss function is optimized to further improve the target detection performance of the network in foggy environments. Finally, transfer learning is applied during the training process, which not only accelerates network convergence and shortens the training time but also further improves the robustness of the network in different environments. Compared with YOLOv5, YOLOv7, and Faster R-CNN networks, the mAP of the improved network increased by 13.57%, 10.3%, and 9.74%, respectively. The results of the comparative experiments from different aspects illustrated that the proposed method significantly enhances the detection performance for vehicle targets in foggy environments.https://www.mdpi.com/1424-8220/25/1/194foggy environmentvisual sensorsvehicle target detectionattention mechanismimage enhancementtransfer learning |
| spellingShingle | Zhaohui Liu Huiru Zhang Lifei Lin Vehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX Network Sensors foggy environment visual sensors vehicle target detection attention mechanism image enhancement transfer learning |
| title | Vehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX Network |
| title_full | Vehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX Network |
| title_fullStr | Vehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX Network |
| title_full_unstemmed | Vehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX Network |
| title_short | Vehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX Network |
| title_sort | vehicle target detection of autonomous driving vehicles in foggy environments based on an improved yolox network |
| topic | foggy environment visual sensors vehicle target detection attention mechanism image enhancement transfer learning |
| url | https://www.mdpi.com/1424-8220/25/1/194 |
| work_keys_str_mv | AT zhaohuiliu vehicletargetdetectionofautonomousdrivingvehiclesinfoggyenvironmentsbasedonanimprovedyoloxnetwork AT huiruzhang vehicletargetdetectionofautonomousdrivingvehiclesinfoggyenvironmentsbasedonanimprovedyoloxnetwork AT lifeilin vehicletargetdetectionofautonomousdrivingvehiclesinfoggyenvironmentsbasedonanimprovedyoloxnetwork |