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.

翻译：旋转对象探测是计算机视觉领域的一个具有挑战性的问题。 空间信息的丢失和参数序列的混乱一直是旋转探测准确性的瓶颈。 在本文中,我们提议了一个基于旋转探测器OSKDet的定向敏感关键点。 我们采用一套关键点来确定目标特征,并预测ROI上的关键点热映射以形成旋转目标。 通过提出方向敏感热映射,OSKDet可以隐含地了解旋转目标的形状和方向,并且具有更强的目标代表模型能力,从而提高本地化精确度并获得高质量的检测结果。 为了在边境地区提取高效的特征,我们设计了一个旋转-自觉变变变变变变模块。 此外,我们根据角度分布探索一个新的关键点重新定序算法和特征聚合模块,以消除关键点秩序的混乱。 几个公共基准的实验结果显示OSKDet的状态和艺术表现。 具体而言,我们分别在DOTA、HRSC2016和UCASADA中实现了77.81%的AP。