Abstract: Aiming at the characteristics of low detection accuracy and poor robustness of traditional target detection and tracking algorithm, as well as the phenomenon of image and video resource redundancy and high vehicle density at the intersection, a real-time traffic flow detection method based on improved YOLOv5 and DeepSort algorithm model is proposed. This experiment uses a data set combin- ing MS COCO  and BDD100k , and uses the improved YOLOv5 algorithm  model to realize the small target vehicle detection in video. Then, the deep learning multi-target tracking algorithm (DeepSort algorithm) is used to carry out real-time tracking and counting of the detected vehicles, and the real-time traffic flow detection of the intersection monitoring end-to-end is realized. By analyzing and comparing models with different parameters, the YOLOv5m model is finally selected. Experimental results show that the proposed method has a faster detection speed and better detection effect for vehicles in complex environments, vehicle occlusion and high target density environments, with an average accuracy of 96.6%. This method can fully meet the requirements of real-time detection of targets, and fully meet the effectiveness of vehicle detection at intersections, and meet the actual requirements of use.

Key words: YOLOv5 algorithm, vehicle detection, DeepSort algorithm, target detection, real-time detection