安徽工程大学学报2024，Vol.39Issue(5)：8-16,9.
基于改进YOLOv5s的道路目标检测算法与跟踪研究
Research on Road Target Detection Algorithm and Tracking Based on Improved YOLOv5s
摘要
Abstract
In the field of automatic driving,due to the complex road scene,the detection accuracy is not high for the existing detection methods,and the detection target is single,so an automatic driving-orien-ted road target detection algorithm based on improved YOLOv5s is proposed.It can realize the simulta-neous detection of vehicles,pedestrians,traffic lights,traffic signs and other targets.First,based on the original model,EIoU loss function is introduced to optimize the prediction frame of YOLOv5 output,which makes the convergence speed faster.The C3 module of the original model was partially replaced by the C2f module of YOLOv8 to improve the precision of small targets.The object detection framework of YOLOv5 is improved to OTA,which can improve the detection speed and reduce the requirements on e-quipment while ensuring the detection accuracy.For ensuring the above three feasible,monocular camera ranging is added to achieve real-time accurate tracking of target distance and early warning of danger.Fi-nally,the data set is established and enhanced to train the data set.Through ablation test,the overall ac-curacy of the improved model is 2％higher than that of the original model,and the probability of train-ing accuracy and recall rate of each target is more than 99％.In the target tracking experiment,the dis-tance can be displayed in real time and the danger can be warned,which proves that the method is feasi-ble and effective.关键词
YOLOv5算法/自动驾驶/图像识别/EIoU损失函数/C2f模块/目标跟踪/危险预警Key words
YOLOv5 algorithm/autonomous driving/image recognition/EIoU loss function/C2f module/target tracking/danger warning分类
计算机与自动化引用本文复制引用
唐杨,王建平,张家高,夏春婷,徐亮亮..基于改进YOLOv5s的道路目标检测算法与跟踪研究[J].安徽工程大学学报,2024,39(5):8-16,9.基金项目
芜湖市科技计划重点研发项目(2020YF53) （2020YF53）
安徽省科技重大专项项目(202103A05020033) （202103A05020033）
