Pedestrian behavior detection and traffic violation recognition based on YOLOv5

This study is dedicated to exploring off-site detection methods to improve the efficiency and accuracy of urban traffic violation recognition, with a special focus on pedestrian red light running. By fusing the attention mechanism and the multi-scale convolutional YOLOv5 pedestrian detection algorithm, a target detection system for off-site environments is designed with high detection accuracy and fast processing speed. The system is able to quickly and accurately identify pedestrians with red-light running behavior with an accuracy of 96.31% after the key steps of preprocessing, feature extraction and target detection of traffic surveillance video images. This research successfully realizes the automatic identification of violation behavior, which is of great theoretical and practical significance for urban traffic safety.