Feature Super-Resolution Fusion With Cross-Scale Distillation for Small-Object Detection in Optical Remote Sensing Images

Abstract

Recently, remote sensing image object detection based on convolutional neural networks (CNNs) has made significant advancements. However, small objects detection remains a major challenge in this field. Because the small size of the object makes it difficult to extract their features and these features are further weakened after downsampling in the network. In order to improve the detection accuracy of small objects in remote sensing images, this letter provides a feature super-resolution fusion framework based on cross-scale distillation. Specifically, we design a sub-pixel super-resolution feature pyramid network (SSRFPN) replacing the bilinear interpolation with sub-pixel super-resolution (SSR) modules to enhance the feature expression capability. Furthermore, we propose a cross-scale distillation (CSD) mechanism to guide the SSR modules in learning the features of small object regions more accurately. Finally, our method is applied to three detectors on two datasets for validation. We adopt YOLOv7 as the baseline model and achieve the best results, with the average precision at a threshold of 0.5 (AP0.5) of 95.0% and 82.3% on the NWPU VHR-10 dateset and DIOR dataset. And the mean average precision of small objects (mAPS) is improved by 8.5% and 2.5%.