In the development of autonomous driving technol-ogy, accurately detecting pedestrians and vehicles in foggy traffic scenarios is a critical challenge. In response to the challenges faced in traditional pedestrian and vehicle detection methods, which of-ten suffer from low precision and reliability, especially in foggy conditions, a modified YOLOv9 network model has been proposed. By integrating the Deeplabv3+ and the SE (Squeeze-and-Excitation) attention mechanism modules into the YOLOv9 model, the method harnesses the simultaneous capture and synthesis of ex-pansive and detailed features across the imagery, augmenting the model's proficiency in recognition and understanding. This en-sures the model's robustness and accuracy under foggy conditions. Experimental results indicate that the YOLOv9 model, after its enhancement and when evaluated on a specially processed VOC format dataset, saw its average precision rate reach 85.9%, marking a 16.5% improvement over the original YOLOv9 network. In foggy traffic conditions, the upgraded YOLOv9 outperforms con-ventional detection networks in tasks related to pedestrian and Vehicle identification.
Enhanced YOLOv9 for Pedestrian and Vehicle Detection in Foggy Traffic Scenarios
25.10.2024
1540409 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
YOLOv9 – BASED TRAFFIC SIGN DETECTION UNDER VARYING LIGHTING CONDITIONS
BASE | 2025
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