Traffic Target Detection Based on Improved YOLO Network
CHEN Zhiwei
LIU Gang
WANG Mengjiao
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.
Keywords：target detectionYOLOv5C3GhostGhostCBAM
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3133-3138 )