In recent years,the object detection method based on deep learning has achieved remarkable results and has been successfully applied to remote sensing.However,due to the wide coverage of remote sensing images,and the small object with less effective information and which is difficult to locate,it is challenging to accurately detect a small object from remote sensing images.To solve this problem,non-local information and context are utilized to improve the quality of small object detection in this paper.First,the detector uses a combination of Refine Feature Pyramid Networks(Refine FPN)and Cross-layer Attention Network(CA-Net)as the backbone,where the Refine FPN obtains richer feature information of a small object,and the CA-Net extracts non-local information and distributes it to each layer evenly.Second,the context transfer module is proposed to transfer the non-local context information to the corresponding region of interest.Finally,the cascade network is used as the detection network to improve the quality of the bounding box of the small object.Experiments are carried out on three remote sensing image datasets,Small-DOTA,DIOR,and OHD-SJTU-S.Experimental results show that the mean average precision(mAP)of the detector proposed in this paper reaches the highest in the three datasets.Among the three categories of ships,vehicles,and windmills that contain more small targets in the DIOR,the average precision(AP)of the detector in this paper is also the highest.This shows that compared with the existing methods,the method in this paper can further improve the detection performance of the small object in remote sensing images.
