基于RCR-YOLO YOLO的红外多尺度目标检测算法的红外多尺度目标检测算法  

Infrared Multi-Scale Target Detection A lgorithm Based on RCR-YOLO

作　　者：陈笑寒 许媛 CHEN Xiaohan;XU Yuanyuan(Departnent ofLogistics Engineering.Shanghai Maritime Universiy,Shanghai 201306,China)

机构地区：[1]上海事大学物流工程学院,上海201306

出　　处：《红外技术》2025年第4期459-467,共9页Infrared Technology

摘　　要：红外目标检测一直在军用和民用领域具有广泛的应用,目前针对在复杂背景下的红外多尺度目标检测中存在的漏检及误检问题,本文提出了一种改进的YOLOv5s算法RCR-YOLO。首先将原YOLOv5s的骨干网络CSPDarkNet53更换为ResNet50,避免了深层网络产生的梯度消失,增强了网络的特征提取能力,然后在骨干网络末端添加CA注意力机制模块,获取不同位置的特征信息,最终在颈部网络中加入Res2Net模块,通过引入多分支结构和逐级增加的分辨率来提高网络的表达能力并可以更好地处理多尺度特征信息,进而增强检测性能。实验结果表明,该方法优于FasterR-CNN、SSD、YOLOv3这些主流的目标检测算法,相较于YOLOv5s,在保持mAP_(50)为99.5%的基础上,将mAP_(50-95)提高了1.1%,拥有更好的检测效果,可以有效地完成复杂背景下的多尺度红外目标检测任务。Infrared target detection has been widely used in both military and civilian fields.To address the issues of missed and false detections in infrared multi-scale target detection under complex backgrounds,an improved YOLOv5s algorithm,RCR-YOLO,is proposed in this paper.First,the backbone network CSPDarkNet53 of the original YOLOv5s was replaced with ResNet50 to avoid gradient vanishing caused by the deep network and to enhance the network’s feature extraction capability.Subsequently,the CA attention mechanism module was added to the end of the backbone to capture feature information from different locations.Finally,the Res2Net module was added to the neck network to improve the network’s representational ability and process multi-scale feature information by introducing a multi-branch structure and progressively increasing resolution,thereby enhancing detection performance.Experimental results show that this method outperforms mainstream target detection algorithms such as Faster R-CNN,SSD,and YOLOv3.Compared to YOLOv5s,mAP_(50–95) increased by 1.1%,while mAP_(50) remained at 99.5%,indicating better detection performance.The algorithm effectively performs multi-scale infrared target detection under complex backgrounds.

关 键 词：红外目标检测 YOLOv5 深度学习 多尺度 

分 类 号：TN219[电子电信—物理电子学]