现代信息科技2024，Vol.8Issue(18)：59-65,7.DOI:10.19850/j.cnki.2096-4706.2024.18.012
基于轻量型YOLOv5和DeepSort的跟车辅助预警算法
Vehicle Tracking Assisted Warning Algorithm Based on Lightweight YOLOv5 and DeepSort
张海川1
作者信息
- 1. 重庆科技大学 电子与电气工程学院,重庆 401331
- 折叠
摘要
Abstract
In view of the problem that the current mainstream vehicle driving assisted algorithm has strong dependence on hardware computing power,a vehicle tracking assisted warning algorithm based on lightweight YOLOv5 and DeepSort is proposed.The algorithm model includes three modules of target detection,target tracking and vehicle tracking warning.It could detect the vehicles ahead of the road in real time,track and measure the distance and speed,and judge the risk level according to the front car distance and speed and make early warning.The algorithm selects YOLOv5s with excellent detection speed and accuracy in the longest time-consuming target detection module and makes lightweight processing,replaces Mobilenetv3 with the backbone skeleton of the original YOLOv5s and introduces GSConv convolution in the feature fusion layer.In addition,aiming at the problem that the convergence effect of the original CIoU loss function is not ideal in the algorithm,the WIoU loss function is introduced.The experimental results show that the number of parameters of the algorithm has decreased by 37%,the weight file size has decreased by 36.1%,the detection speed has increased by 23.8%and the mAP value has decreased by only 0.3%compared with directly using YOLOv5s as the detection module.关键词
车辆检测/目标追踪/神经网络Key words
vehicle detection/target tracking/neural network分类
信息技术与安全科学引用本文复制引用
张海川..基于轻量型YOLOv5和DeepSort的跟车辅助预警算法[J].现代信息科技,2024,8(18):59-65,7.
