改进YOLOv5s算法的车辆目标实时检测方法
A Real-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm
陈秀锋 1王成鑫 1吴阅晨 1谷可鑫1
作者信息
- 1. 青岛理工大学 土木工程学院,山东 青岛 266520
- 折叠
摘要
针对城市道路车辆检测中小目标车辆漏检率高和存在异类冗余框的问题,提出一种改进YOLOv5s的车辆实时检测算法.对YOLOv5s算法网络结构进行优化,采用增加小目标检测层,将浅层特征图与深层特征图拼接后进行检测的方法,提升小目标车辆的检测率;针对异类冗余框问题,采用加权非极大值抑制融合两边框信息的方法,提升检测准确性.实验结果表明,改进YOLOv5s算法的平均检测精度(mAP@0.5∶0.95)达到 64.17%,相比YOLOv5s算法,查准率、召回率分别提高1.72%、0.72%;在小目标车辆检测中,正检率提高5.95%,漏检率降低4.63%.改进YOLOv5s算法能有效改善小目标车辆的检测精度和准确率.
Abstract
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.
关键词
车辆检测/深度学习/改进YOLOv5s算法/小目标检测/异类冗余框Key words
vehicle detection/deep learning/the improved YOLOv5 algorithm/small target detection/heterogeneous redundant frames引用本文复制引用
基金项目
国家自然科学基金(52272311)
国家自然科学基金青年基金(62003182)
出版年
2024
NETL
NSTL
万方数据