Foreground Refinement Network for Rotated Object Detection in Remote Sensing Images

Object detection has been a fundamental task in the field of remote sensing and has made considerable progress in recent years. However, the high background complexity in remote sensing images (RSIs) remains challenging. In this article, we propose a refined rotation detector, namely, the Foreground Refinement Network (FoRDet), to alleviate the above problem by leveraging the information of foreground regions from the perspectives of feature and optimization. Specifically, we propose a foreground relation module (FRL) that aggregates the foreground-contextual representations from the coarse stage and improves the discrimination of foreground regions on feature maps in the refined stage. Besides, considering the risk of the potential foreground anchors being overwhelmed in the training phase, we design a foreground anchor reweighting (FRW) loss that integrates the classification confidence and localization accuracy of each foreground anchor from the coarse stage to dynamically regulate their contributions in the refined stage, which highlights the potential foreground anchors. The comprehensive experimental results on three public datasets for rotated object detection DOTA, HRSC2016, and UCAS-AOD demonstrate the effectiveness of our proposed method.