Aiming at the lack of detection accuracy about remote sensing target detection,an improved YOLOv5s remote sensing target detection algorithm was proposed to improve the detection accuracy of remote sensing targets.CSP-D module was used for feature extraction in backbone network,and the feature information of the shallow and deep layers of the network was fully uti-lized for feature enhancement.The neck network used BiFPN structure for feature fusion to improve the efficiency of multi-scale feature information fusion.Experimental results show that for the remote sensing target dataset DIOR,compared with the origi-nal network,the mean average precision(mAP)is increased by 2.1％through the improved YOLOv5s network.The average precision(AP)of different object detection classes is improved.The problems of missed detection and false detection exist in the original network detection are alleviated.The improved network detection speed can still meet the real-time requirements and shows better detection performance.
维普
万方数据