现代信息科技2024，Vol.8Issue(22)：25-29,35,6.DOI:10.19850/j.cnki.2096-4706.2024.22.006
Ship-YOLOv8:一种轻量级高分辨遥感图像船舶细粒度检测算法
Ship-YOLOv8:A Lightweight Ship Fine-grained Detection Algorithm of High-resolution Remote Sensing Images
摘要
Abstract
Aiming at the characteristics of large intra-class differences,high similarity between classes,large scale changes of objects and scenes,difficulty in feature extraction,and small samples in the ship fine-grained target detection and classification task of high-resolution images,an improved algorithm based on YOLOv8 is proposed.Firstly,the SimAM Attention Mechanism is introduced into the backbone network to make the algorithm model more focused on the ship object when running in the complex background.Secondly,the SPD-Conv module is introduced in the neck to improve the problems of large ship scale changes and small target detection in complex backgrounds.Finally,for the characteristics of fine-grained ship target detection,it replaces the Mish activation function and Focal-Loss loss function to speed up model convergence and improve model accuracy.Comparative experiments show that the improved algorithm achieves a detection accuracy of 94.49%in the FAIR1M_Ship dataset while ensuring the detection speed and number of model parameters.Compared with the currently popular target detection algorithms,the detection accuracy of the improved algorithm has been improved to a certain extent.关键词
船舶/目标识别/遥感图像/细粒度识别/YOLOv8Key words
ship/target recognition/remote sensing image/fine-grained recognition/YOLOv8分类
信息技术与安全科学引用本文复制引用
陈燕奎,龙超活,何骏杰,张豫,谢作轮..Ship-YOLOv8:一种轻量级高分辨遥感图像船舶细粒度检测算法[J].现代信息科技,2024,8(22):25-29,35,6.基金项目
广东省基础与应用基础研究基金自然科学基金(321B0104) （321B0104）
教育部产学合作协同育人项目(220702313062517) （220702313062517）
广东省科技创新战略专项资金(大学生科技创新培育)项目(pdjh2022b0485,pdjh2024b350) （大学生科技创新培育）
