Research on traffic sign detection algorithm in complex weather

Under complex weather conditions, the vehicle vision system has low recognition accuracy for traffic signs, and there are problems such as missed detection and false detection. An improved YOLOX-S traffic sign detection model is proposed. Firstly, a detection layer is added to the YOLOX-S network, so that the model can effectively detect the minimum target and improve the prediction ability of the model. Then the attention mechanism module is added to the YOLOX-S feature fusion network to strengthen the feature extraction function of the network. Finally, the data enhancement mechanism is introduced to the model, so that the detection of the model in severe weather such as haze and rain and snow has strong robustness. The detection model was tested on the Chinese Traffic Sign Detection Benchmark dataset (CCTSDB). The results show that the mAP @ 0.5 of the improved algorithm is 2.67 % higher than that of the original YOLOX-S algorithm, and the mAP @ 0.5: 0.95 is 4.58 % higher than that of the original YOLOX-S algorithm. The model meets the requirements of real-time detection.