Oriented RepPoints for Aerial Object Detection
OrientedRepPoints
Oriented RepPoints employs a set of adaptive points to capture the geometric and spatial information of the arbitrary-oriented objects, which is able to automatically arrange themselves over the object in a spatial and semantic scenario. To facilitate the supervised learning, the oriented conversion function is proposed to explicitly map the adaptive point set into an oriented bounding box. Moreover, we introduce an effective quality assessment measure to select the point set samples for training, which can choose the representative items with respect to their potentials on orientated object detection. Furthermore, we suggest a spatial constraint to penalize the outlier points outside the groundtruth bounding box. In addition to the traditional evaluation metric mAP focusing on overlap ratio, we propose a new metric mAOE to measure