红外技术2026，Vol.48Issue(1)：36-44,9.
基于改进YOLOv5s的车载红外图像目标检测算法
Vehicle-Infrared-Image Target Detection Algorithm Based on Improved YOLOv5s
摘要
Abstract
In-vehicle infrared images can help drivers identify pedestrians and other vehicles on the road at night and during bad weather,thereby reducing traffic accidents.To address the low detection accuracy of vehicle infrared images using the YOLOv5s algorithm,an improved YOLOv5s algorithm for vehicle infrared image target detection is proposed.First,a receptive field enhancement structure,namely an RFENeck module,is designed,which replaces the BottleNeck module in C3,to enhance the receptive field area of the feature fusion network and thus improve the detection accuracy.Second,a dynamic object detection head,combined with an attention mechanism,is used to improve the expression ability of the detection head.Finally,to eliminate the increase in model size caused by the improvement,an efficient backward residual mobile module,combined with the cascade designs of convolutional neural networks and Transformer,is used to form the backbone network.This module can reduce the number of network parameters and calculation steps without reducing the accuracy.The experimental results show that compared to YOLOv5s,the average detection accuracy of the improved algorithm increases from 82.9％to 85.0％;in addition,the computation amount is reduced by 5.7％,and the model weight is reduced by 0.4 M.These results indicate that the proposed algorithm fulfils the requirements of model size and accuracy.关键词
目标检测/红外图像/YOLOv5/动态检测头Key words
target detection/infrared images/YOLOv5/dynamic head分类
信息技术与安全科学引用本文复制引用
姜宇,刘冉冉,李杭宇,李峰,郭威..基于改进YOLOv5s的车载红外图像目标检测算法[J].红外技术,2026,48(1):36-44,9.基金项目
国家自然科学基金项目(62003150 （）
62003151 （）
52105260) （）
江苏省高等学校基础科学(自然科学)面上项目(21KJB120002) （自然科学）
江苏高校"青蓝工程"资助 （）
"江苏理工学院中吴青年创新人才支持计划"资助 （）
江苏省研究生培养创新工程项目(SJCX23_1605). （SJCX23_1605）
