Small object detection in remote sensing images based on super-resolution

•The proposed Super-Resolution method can significantly improve the detection accuracy.•Perception and texture matching loss are combined to design the loss function.•The proposed method achieves 74.47% mAP, which is 0.79% better than that of S2A-NET. Accurate objects detection in remote sensing images is very important, because security, transportation, and rescue applications in military and civilian fields require fully analyzing and using these images. To address the problem that many small-sized objects in remote sensing images are difficult to detect, this paper proposes an improved S2ANET-SR model based on S2A-NET network. In this paper, the original and reduced image are fed to the detection network at the same time, and then a super-resolution enhancement module for the reduced image is designed to enhance the feature extraction of small objects, after that, the perceptual loss and texture matching loss is proposed as supervision. Extensional experiments are conducted to evaluate the performance on the general remote sensing dataset DOTA, and the results show that our proposed method can achieve 74.47% mAP, which is 0.79% better than the accuracy of S2A-NET.