SRAF-Net: A Scene-Relevant Anchor-Free Object Detection Network in Remote Sensing Images

Object detection is a fundamental and important task in the analysis of remote sensing images (RSIs), and existing deep learning-based object detection models in this literature strongly rely on predefined anchor boxes and encounter redesigned difficulties related to anchors. In addition, they often ignore the scene-contextual information that objects are usually closely related to their surrounding scene. To deal with these problems, we propose an anchor-free network, referred to as scene-relevant anchor-free network (SRAF-Net), for object detection in RSIs. The SRAF-Net first captures the scene-contextual features of objects by using a designed scene-enhanced feature pyramid network (SE-FPN) and then performs more accurate detection by implementing a scene auxiliary detection head (SADH), which can predict the existence of the objects with the help of the scene-contextual features extracted from the SE-FPN. To deal with insufficient scene diversity in the training stage, a simple yet effective data augmentation module, termed balanced mixup data augment (BMDA), is introduced by linearly expanding the training dataset to improve the generalization of SRAF-Net. Comprehensive experiments on three publicly available challenging remote sensing datasets demonstrate the effectiveness of the proposed method. The codes will be made publicly available at https://github.com/Complicateddd/SRAF-Net.