AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm - CHEN Xiufeng; WANG Chengxin; WU Yuechen; GU Kexin (Journal of Harbin University of Science and Technology)
Aiming at the high missed detection rate of small target vehicles and the heterogeneous redundant frames in video vehicle detection，a real-time vehicle detection algorithm based on improved YOLOv5s was proposed. To improve the detection rate of small target vehicles，an optimization of the YOLOv5s algorithm network structure was established，which added a small target detection layer and spliced the shallow feature map with the deep feature map in the detection. For the problem of heterogeneous redundant frames，weighted non-maximum value suppression is used to fuse the information of both frames to improve the detection accuracy. The experimental results show that the average detection accuracy ( mAP @ 0. 5 ∶ 0. 95 ) of the improved YOLOv5s algorithm reaches 64. 17% . Compared with the YOLOv5s algorithm，the precision and recall rate are improved by 1. 72% and 0. 72% respectively. In the small target vehicle detection，the positive detection rate is increased by 5. 95% and the missed detection rate is reduced by 4. 63% . The improved YOLOv5s algorithm can effectively improve the detection precision and accuracy of small target vehicles.