  Rotated object detection is a challenging issue of computer vision field.
Loss of spatial information and confusion of parametric order have been the
bottleneck for rotated detection accuracy. In this paper, we propose an
orientation-sensitive keypoint based rotated detector OSKDet. We adopt a set of
keypoints to characterize the target and predict the keypoint heatmap on ROI to
form a rotated target. By proposing the orientation-sensitive heatmap, OSKDet
could learn the shape and direction of rotated target implicitly and has
stronger modeling capabilities for target representation, which improves the
localization accuracy and acquires high quality detection results. To extract
highly effective features at border areas, we design a rotation-aware
deformable convolution module. Furthermore, we explore a new keypoint reorder
algorithm and feature fusion module based on the angle distribution to
eliminate the confusion of keypoint order. Experimental results on several
public benchmarks show the state-of-the-art performance of OSKDet.
Specifically, we achieve an AP of 77.81% on DOTA, 89.91% on HRSC2016, and
97.18% on UCAS-AOD, respectively.
