Research on object detection in remote sensing image based on YOLOv7-RS
Aiming at the problems of complex background,obscure object features and dense array of small targets in remote sensing image target detection,we propose an improved remote sensing image target detection algorithm Yolov7-RS(Yolov7-Re-mote Sensing)based on the YOLOv7 algorithm,which improves the target detection accuracy of remote sensing image.Firstly,SimAM is integrated into feature extraction network to reduce the interference of background noise.Secondly,D-ELAN network enhanced feature extraction capability of remote sensing objects is proposed.Thirdly,SIOU loss function is used to improve the convergence rate of the algorithm model.Finally,the allocation strategy of positive and negative samples is optimized to improve the problem of missing detection when small objects are densely arranged in remote sensing images.Experimental results show that the mAP of YOLOv7-RS on NWPU VHR-10 data sets and DOTA data sets reaches 95.4%and 74.1%,which is significantly im-proved compared with other mainstream algorithms.
万方数据
维普
