海洋测绘2025，Vol.45Issue(1)：66-71,6.DOI:10.3969/j.issn.1671-3044.2025.01.015
基于改进YOLOv5的飞机目标检测算法
Aircraft target detection algorithm based on improved YOLOv5
摘要
Abstract
Due to the size and shape differences of aircraft targets,occlusion,dense distribution and complex background,the existing models have high error and missed detection in the detection process.To this end,this paper proposes an aircraft target detection model based on an improved YOLOv5,called AT YOLOv5.Firstly,the coordinate attention module is integrated in the backbone network to enhance the feature extraction ability of the model.Then,aiming at the problem that the multi-scale representation ability of FPN is reduced during feature fusion,an attention feature fusion network is proposed,which can realize the adaptive fusion of features of different scales based on attention weight.Finally,the small target detection layer is improved,and the Swin Transformer module is added to all the detection layers to enhance the ability of the network model to obtain global information and associate target information.In the experimental part,DOTA and RSOD datasets are used to verify the effectiveness and generalization ability of the model.The experimental results show that the AP50 of the detection algorithm proposed in this paper is 3.9%higher than that of the YOLOv5s network under the DOTA dataset,and the AP50:95 is 1.0%higher than that of the YOLOv5s network.The FPS of high-resolution remote sensing images can reach 64,and the AP50 of the detection algorithm under the RSOD dataset can also reach 96.7%.The proposed algorithm can effectively realize the aircraft target detection task,and has good detection accuracy,real-time performance and robustness.关键词
飞机目标检测/深度学习/YOLOv5网络/注意力机制优化/特征融合Key words
aircraft object detection/deep learning/YOLOv5 network/optimization of the attention mechanism/feature fusion分类
天文与地球科学引用本文复制引用
张贝贝,刘建辉,王鑫,魏祥坡,麻顺顺..基于改进YOLOv5的飞机目标检测算法[J].海洋测绘,2025,45(1):66-71,6.基金项目
国家自然科学基金项目(41801388) （41801388）
河南省自然科学基金项目(222300420386). （222300420386）
