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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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 25; H. 1; S. 194
Hauptverfasser: Liu, Zhaohui, Zhang, Huiru, Lin, Lifei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.01.2025
MDPI
Schlagworte:
Accuracy
Algorithms
attention mechanism
Computational linguistics
Deep learning
Fog
foggy environment
image enhancement
Language processing
Methods
Natural language interfaces
Neural networks
Parameter estimation
Sensors
transfer learning
vehicle target detection
Vehicles
visual sensors
transfer learning
image enhancement
attention mechanism
vehicle target detection
foggy environment
visual sensors
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25010194