红外技术2025，Vol.47Issue(10)：1234-1245,12.
CSM-YOLO:红外弱小目标检测算法
CSM-YOLO:Infrared Weak Small Target Detection Algorithm
摘要
Abstract
To address the issues of low detection performance,high missed detection rate,and false alarm rate in an infrared small-target detection caused by the small pixel size of weak targets and complex background interference,this study proposes an improved infrared dim and small target detection algorithm based on YOLOvl2n,named CSM-YOLO.First,a CA-DEConv module was designed to replace the C3k2 module in the backbone network,enhancing the extraction capability of edge and contour information for weak and small targets.Second,the SCSA attention mechanism was introduced to enable the model to focus dynamically on key regions of small target features.Finally,a multiscale feature fusion module was designed to adaptively fuse features across different scales,reducing the loss of small-target information in deeper network layers and improving the overall detection performance.Experimental results on the public datasets SIRST,SIRST V2,and NUDT-SIRST demonstrate that the proposed CSM-YOLO improves mAP50 by 3％,13.3％,and 13.2％,respectively,and mAP50:95 by 0.8％,5.9％,and 15.5％,respectively,compared with the baseline YOLOvl2n model,while also reducing the total number of parameters by 3.5％.关键词
红外图像/YOLOv12n/目标检测/弱小目标Key words
infrared images/YOLOv12n/object detection/weak small target分类
计算机与自动化引用本文复制引用
赵洋,杨聪,陈蓉,范柳,徐森..CSM-YOLO:红外弱小目标检测算法[J].红外技术,2025,47(10):1234-1245,12.基金项目
辽宁省教育厅基本科研项目面上项目(LJKMZ20220782). （LJKMZ20220782）
