CRKD-YOLO: Cross-Resolution Knowledge Distillation for Low-Resolution Remote Sensing Image Object Detection

The majority of advanced remote sensing object detection technologies excel in accurately detecting objects from high-resolution images. However, in practical scenarios, it is often necessary to detect objects in images of varying resolutions due to differences in imaging equipment. When dealing with lower resolution images, the limited detailed information and blurry boundaries lead to a noticeable decrease in detection accuracy. To address this problem, we propose a efficient object detection method for low-resolution remote sensing images based on YOLO detector, named CRKD-YOLO. The method constructs a Cross-Resolution Knowledge Distillation (CRKD) framework to resolve the issue of feature mismatch, enabling the model with low-resolution inputs to learn more refined feature representations from high-resolution images. Furthermore, to effectively leverage the limited detailed information in low-resolution images, we propose the Backbone Augment Feature Pyramid Network (BAFPN). It enhances detection accuracy for low-resolution remote sensing images while making the model more lightweight. Massive experiments on DOTA, DIOR, NWPU VHR-10, DroneVehicle and VEDAI demonstrate that our CRKD-YOLO achieves significant improvements, even achieving higher accuracy compare to training and testing high-resolution images with baseline. Our code is published at https://github.com/Jianfantasy/CRKD-YOLO.