Multi-Oriented Rotation-Equivariant Network for Object Detection on Remote Sensing Images

Object detection has attracted a lot of attention in the field of image automatic interpretation. Detectors based on convolution neural networks (CNNs) applied in natural scene images encode detection results with horizontal bounding boxes (HBBs), which can not accurately calibrate the position and shape of the arbitrary-orientation objects on remote sensing images (RSIs). To solve these issues, we propose an object detection framework named multi-oriented rotation-equivariant network (MORE-Net) in this letter. The MORE-Net consists of a multi-oriented rotating filter (MORF) and a multi-oriented ground objects detector. The MORF generates rotation-equivariant features by rotating canonical filter according to the predefined discrete directions. Equipped with MORF, the multi-oriented ground objects detector extracts rotation-invariant semantic representations for each decoded rotated region of interest (RRoI). We also propose area smooth (AS) L1 loss to impose tighter shape and position constraints to the RROIs. Extensive experiments and comprehensive evaluations on the large-scale DOTA dataset demonstrate the effectiveness of the proposed framework, which achieves a mean average precision (mAP) value of 81.27 on the DOTA-v1.0 dataset and a mAP value of 78.03 on the DOTA-v1.5 dataset.