红外技术2026，Vol.48Issue(5)：544-553,10.
基于改进YOLOv8的红外船舶目标检测算法
Infrared Ship Target Detection Algorithm Based on the Improved YOLOv8
摘要
Abstract
In infrared imaging,moving ship targets often lack clear intuitive information,such as size,shape,and texture,resulting in low detection accuracy and false detections of small targets in complex sea and sky backgrounds.Therefore,this study proposes an infrared ship detection method by improving YOLOv8.First,a hybrid convolution module was designed,and dynamic offset technology was used to adjust the sampling position to address the diversity of target shapes and better adapt to target characteristics at different scales by providing multiple convolution kernel parameters.Second,the SimAM parameter-free attention mechanism was introduced after SPPF to focus on key areas of the image while reducing computational complexity.The non-maximum suppression algorithm was then optimized to reduce missed detections caused by the suppression of small target detection boxes by large target detection boxes.Finally,the loss function was optimized by replacing the original loss function with EIoU to improve target localization accuracy and overall detection performance.Experimental results show that the improved algorithm achieved a 94.67%mAP on the infrared ship dataset,representing an increase of 3.83%compared with the original mAP.Compared with other classic algorithms,each evaluation index was improved to varying degrees,which demonstrates that the improved algorithm achieves better detection performance and can satisfy the real-time detection task of infrared targets.关键词
红外船舶检测/混合卷积模块/注意力机制/非极大值抑制/损失函数Key words
infrared ship detection/hybrid convolution module/attention mechanism/non-maximum suppression/loss function分类
信息技术与安全科学引用本文复制引用
缪兰,高德勇,石文玉..基于改进YOLOv8的红外船舶目标检测算法[J].红外技术,2026,48(5):544-553,10.基金项目
甘肃省高校科研 创新平台重大培育项目(2024CXPT-17) （2024CXPT-17）
安徽省质量工程项目、网络工程卓越人才培养改造提升(2022zygzts048) （2022zygzts048）
安徽新华学院博士人才培养项目(bs2025kyqd118). （bs2025kyqd118）
