计算机与数字工程2025，Vol.53Issue(11)：3133-3138,6.DOI:10.3969/j.issn.1672-9722.2025.11.025
基于改进YOLO网络的交通目标检测
Traffic Target Detection Based on Improved YOLO Network
陈志伟 1刘罡 2王梦姣1
作者信息
- 1. 南京信息工程大学电子与信息工程学院 南京 210044
- 2. 无锡学院电子信息工程学院 无锡 214105
- 折叠
摘要
Abstract
Traffic target detection technology is of great significance for realizing road traffic safety and automatic driving tech-nology.At present,the most advanced target detection algorithm is a kind of target detection algorithm,such as YOLOv5.However,YOLOv5 has some problems,such as large amount of computation and parameters,and high detection error rate.The solution pro-posed in this paper is to replace the C3 module in the neck of YOLOv5 with the C3Ghost module and replace the convolution module with the Ghost module to reduce the model parameters,and integrate the CBAM attention mechanism module into the C3 module of the backbone network to highlight the key information of the object,increase the network feature extraction ability,and reduce the false detection rate.The experimental results show that the improved algorithm improves the mAP by 1%,reduces the computational load FLOPs by 2.4 G,and reduces the parameter quantity by 1.3×106 on the basis of YOLOv5.The model is lightweight while ensur-ing the accuracy.关键词
目标检测/YOLOv5/C3Ghost/Ghost/CBAMKey words
target detection/YOLOv5/C3Ghost/Ghost/CBAM分类
信息技术与安全科学引用本文复制引用
陈志伟,刘罡,王梦姣..基于改进YOLO网络的交通目标检测[J].计算机与数字工程,2025,53(11):3133-3138,6.
