Fine-Grained Object Detection in Remote Sensing Images via Adaptive Label Assignment and Refined-Balanced Feature Pyramid Network

Object detection in high-resolution remote sensing images remains a challenging task due to the uniqueness of its viewing perspective, complex background, arbitrary orientation, etc. For fine-grained object detection in high-resolution remote sensing images, the high intra-class similarity is even more severe, which makes it difficult for the object detector to recognize the correct classes. In this article, we propose the refined and balanced feature pyramid network (RB-FPN) and center-scale aware (CSA) label assignment strategy to address the problems of fine-grained object detection in remote sensing images. RB-FPN fuses features from different layers and suppresses background information when focusing on regions that may contain objects, providing high-quality semantic information for fine-grained object detection. Intersection over Union (IoU) is usually applied to select the positive candidate samples for training. However, IoU is sensitive to the angle variation of oriented objects with large aspect ratios, and a fixed IoU threshold will cause the narrow oriented objects without enough positive samples to participate in the training. In order to solve the problem, we propose the CSA label assignment strategy that adaptively adjusts the IoU threshold according to statistical characteristics of oriented objects. Experiments on FAIR1M dataset demonstrate that the proposed approach is superior. Moreover, the proposed method was applied to the fine-grained object detection in high-resolution optical images of 2021 Gaofen challenge. Our team ranked sixth and was awarded as the winning team in the final