Vehicle and pedestrian detection method based on improved YOLOX in foggy environment

Abstract

Most of the current vision sensor-based target detection is suitable for good weather conditions. Adverse weather conditions, especially foggy environments, significantly reduce visibility, which seriously affects the target detection performance. To improve driving safety in foggy environments, this paper proposes an improved YOLOX-based vehicle and pedestrian detection method in foggy environments. The method is based on the advanced YOLOX network model and introduces an attention mechanism in the feature extraction network to enhance the network's extraction of target features in foggy images. Some images in the training dataset are fogged to supplement the target-specific features in foggy environments and improve the robustness of the target detection network in foggy environments. The idea of migration learning is used in the training process to save training time and optimize the training effect. The experimental results show that the target detection method proposed in this paper has significantly improved the detection performance of vehicles and pedestrians in the foggy environment, with an 11.35% improvement in mAP, and the detection effect is better than the GCANet image defogging method. The effectiveness of the method improvement is proved.