燕山大学学报2026，Vol.50Issue(2)：130-137,8.DOI:10.3969/j.issn.1007-791X.2026.02.004
基于改进YOLOv5的遥感图像目标检测算法
Remote sensing image object detection algorithm based on improved YOLOv5
摘要
Abstract
In order to solve the problem of low target detection accuracy caused by dense array of remote sensing images,large scale difference and complex background,an improved YOLOv5 based remote sensing image target detection algorithm is proposed.First,based on the YOLOv5 network framework,a triplet attention mechanism is added to the C3 structure of the feature fusion network to improve the feature fusion capability of the model.Secondly,a large selective kernel network is added to the backbone network and feature fusion network to adjust the large spatial receptive field and better simulate the ranging environment of various objects in the remote sensing scene.Next,the crossover ratio based on minimum points is used as a new bounding box regression method to improve the speed and precision of bounding box regression.Finally,a new non-maximum suppression algorithm is used to reduce the missed detection of dense targets.The proposed algorithm was tested on DIOR,a public remote sensing data set.The results show that the proposed algorithm has an average accuracy increase of 6.6%compared with the original YOLOv5 algorithm.Compared with other YOLO detection algorithms and their improved algorithms,the proposed algorithm has the highest detection accuracy on the small sample data set used.关键词
遥感图像/目标检测/三重注意力机制/大选择性核网络/边界框回归/非极大抑制Key words
remote sensing image/target detection/triplet attention/large selective kernel network/bounding box regression/non-maximum suppression分类
信息技术与安全科学引用本文复制引用
金梅,王泓沣,张立国,张琦,袁煜淋..基于改进YOLOv5的遥感图像目标检测算法[J].燕山大学学报,2026,50(2):130-137,8.基金项目
国家重点研发计划资助项目(2020YFB1711001) （2020YFB1711001）
河北省军民融合产业发展专项资金项目(2018B190) （2018B190）
