Target Detection of Remote-Sensing Images Based on Improved YOLOv5
Although target detection technology has advanced,many challenges still exist in the detection of remote-sensing images.An improved YOLOv5-based remote-sensing image target detection algorithm is proposed to address the issues of low target detection accuracy caused by complex backgrounds,large target scale differences,and arbitrary target orientation in remote-sensing images.First,a joint multiscale feature enhancement network with attention is constructed to fully fuse high-level and low-level features such that the feature layers contain semantic and rich detailed information.During the fusion process,the designed feature focusing module is used to help the model select key features and suppress irrelevant information.Second,a Receptive Field Block(RFB)is used to update the fused feature map and expand the receptive field of the feature map to reduce feature information loss.Finally,by adding rotation angles to the targets and using circular smooth labels to transform the regression problem into a classification problem,the accuracy of remote-sensing target localization is improved.The experimental results on the a large-scale Dataset for Object deTection in Aerial images(DOTA)show that compared with the YOLOv5 algorithm,the mean Average Precision(mAP)when the Intersection over Union(IoU)values of the proposed algorithm are 0.5 and 0.5-0.95(mAP@0.5 and mAP@0.5:0.95)increase by 7.3 and 3.3 percentage points,respectively.This can significantly improve the detection accuracy of remote-sensing image targets in a complex background and improve the missing and false detection of remote-sensing targets.
target detectionremote-sensing imagefeature fusionReceptive Field Block(RFB)circular smooth label
万方数据
维普
