RotPointNet: Keypoint based Oriented Object Detection for Aerial Image

Remote sensing object detection is characterized by arbitrary direction, dense targets and variable scale. Like common object detection methods predict horizontal rectangle boxes directly, most of the existing remote sensing oriented object detection methods predict oriented rectangle boxes directly. i.e. Center point position, length, width and rotation angle of the oriented rectangle boxes. These models often need to design complex rotation detection module to adapt to the rotating characteristics of targets, they lack good performance on targets with large aspect ratio changes as well. In this paper, we propose a keypoint based oriented object detection method for aerial images named RotPointNet to improve detection accuracy of rotating targets with large aspect ratio change. RotPointNet first locates the two endpoints of rotating targets using keypoint detection method, and then constructs the remote sensing oriented target based on the endpoints and additional width information. In addition, a new method of matching target key points is proposed in this paper. We conduct experiments to prove, the target detection model RotPoint-Net and key point matching strategy proposed in this paper can achieve good detection results on remote sensing images, especially for the detection of targets with large aspect ratio changes.