无线电工程2025，Vol.55Issue(3)：493-499,7.DOI:10.3969/j.issn.1003-3106.2025.03.005
改进YOLOv5的道路车辆目标检测方法
Improved YOLOv5 for Road Vehicle Object Detection
摘要
Abstract
The mainstream road vehicle object detection algorithms have low recognition accuracy for small targets in complex environments,and are prone to missed detections and false detections due to occlusion and inaccurate positioning.An improved version of YOLOv5 algorithm is proposed.For small targets on the road,the Head detection layer structure is improved and a large-scale target detection layer is added to improve the accuracy of small target detection on the road.To adapt to the diverse shape and scale changes of the target,Omni-Dimensional Dynamic Convolution(ODConv)is introduced into the neck network to replace the original convolution module and improve the feature extraction ability.In order to fully utilize global information,a Global Attention Mechanism(GAM)is introduced into the neck network to enhance feature extraction capabilities.To address the issue of positioning accuracy,the MPDIoU loss function is introduced to make the predicted box more consistent with the actual box.The experimental results show that the improved YOLOv5 algorithm achieves a mean Average Precision(mAP)of 88.7%on the autonomous driving dataset KITTI,an increase of 2%compared to the benchmark model,and a 12%increase in Frames per Second(FPS).The improved algorithm,with higher detection accuracy and faster detection speed,effectively improves the object detection in complex road conditions.关键词
道路目标检测/YOLOv5/特征提取/MPDIoU/注意力机制Key words
road object detection/YOLOv5/feature extraction/MPDIoU/attention mechanism分类
计算机与自动化引用本文复制引用
李康,宋文广..改进YOLOv5的道路车辆目标检测方法[J].无线电工程,2025,55(3):493-499,7.基金项目
国家科技重大专项(2021DJ1006)National Science and Technology Major Project(2021DJ1006) （2021DJ1006）
