摘要：As the core technology in the field of intelligent transportation, traffic sign detection and recognition play a significant role in unmanned driving, high-precision map navigation, and auxiliary driving. However, due to the inevitable extreme weather and human impacts in the process of driving on the road, traffic sign detection and recognition remains a difficult problem. In this paper, a traffic sign detection and recognition algorithm based on the improved YOLOv4 is proposed to improve the original Feature Pyramid Network, so that the feature layer has more semantic information. Additionally, the coordinate attention mechanism module is added to the model, which fully considers the relationship between channels and position information. An adaptive feature fusion module is also added before the detection head to solve the problem of unclear semantic information caused by scaling in target detection. The experimental results indicate that the detection accuracy of the improved YOLOv4 algorithm is increased by 5%, and the detection performance is better than that of other target detection algorithms.
