Research on object detection in remote sensing image based on YOLOv7-RS
Liang Qi 1,2，Yang Xiaowen 2,3,4
1 General Staff of Shanxi PAP, Taiyuan 030012, China; 2 College of Computer Science and Technology, North University of China, Taiyuan 030051, China;3 Shanxi Key Laboratory of Machine Vision and Virtual Reality, Taiyuan 030051, China; 4 Shanxi Province′s Vision Information Processing and Intelligent Robot Engineering Research Center, Taiyuan 030051, China
Abstract： 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 Remote 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 improved compared with other mainstream algorithms.
