Rotated R-CNN: A Two-Stage Object Detection Method Adapted To Oriented Bounding Boxes

Currently, oriented object detection, as an emerging subfield within object detection, has garnered significant attention. Besides encompassing directional information, datasets of oriented objects exhibit notable characteristics, including significant variations in object scales and a wide range of aspect ratios for ground-truth bounding boxes. Nevertheless, the current state-of-the-art two-stage rotating object detection models have not sufficiently addressed these characteristics, leading to inherent limitations in accuracy. In response to these challenges, we introduce the Rotated RCNN. Our model is the first to introduce trainable anchors in the field of oriented object detection to achieve anchor distributions similar to the ground truth boxes in the oriented object dataset. Furthermore, considering the distinctive traits of oriented ground truth boxes, we have devised a novel strategy for assigning labels to more effectively choose positive and negative samples specifically designed for oriented objects. In the regression phase of the RPN, we introduce shape constraints to alleviate accuracy losses stemming from mismatches between the encoding method and oriented objects. We comprehensively evaluate our model on the DOTAv1.0 and HRSC2016 datasets, demonstrating the effectiveness of our meticulously designed model.