桂林电子科技大学学报2024，Vol.44Issue(4)：416-426,11.DOI:10.3969/1673-808X.2022328
基于改进YOLOv5s的监控视频车流量检测
Surveillance video vehicle flowrate detection based on improved YOLOv5s
摘要
Abstract
Aiming at the problems of low detection and tracking accuracy,poor robustness and low statistical accuracy of traffic flow of multi-type vehicles in video,a vehicle flow detection method based on improved YOLOv5s object detection algorithm and Deep-Sort tracking algorithm is proposed.This method reconstructs the feature extraction network of YOLOv5s to strengthen the extrac-tion of important features of the target and improve the detection accuracy of the detector.Firstly,the Swin Transformer module is introduced in the Backbone network to replace some C3 modules in the original algorithm,so as to enhance the global modeling ability of the model,better capture contextual feature information,and expand the receptive field of the model.Then,by comparing different attention mechanisms,GAM attention is selected in Neck network to enhance the cross-dimensional interaction of informa-tion between channels and spatial dimensions,reduce information loss and enhance network performance.Finally,the feature extrac-tion network part of the DeepSort tracking algorithm is optimized and re-trained on the vehicle re-recognition dataset to make it more suitable for vehicle tracking.Experimental results show that the improved YOLOv5s improves by 2.04％points compared with the original algorithm.Combined with DeepSort algorithm,the statistical accuracy of vehicle traffic in different lighting conditions such as day,evening and night can reach 97.5％,95.7％and 85.1％,respectively.关键词
YOLOv5s目标检测/DeepSort目标跟踪/Swin Transformer/GAM/车流量检测Key words
YOLOv5s target detection/DeepSort target tracking/Swin Transformer/GAM/vehicle flow detection分类
信息技术与安全科学引用本文复制引用
熊显名,刘雨鑫,黎恒..基于改进YOLOv5s的监控视频车流量检测[J].桂林电子科技大学学报,2024,44(4):416-426,11.基金项目
国家自然科学基金(61965005) （61965005）
广西自然科学基金(2019GXNSFDA185010) （2019GXNSFDA185010）
