  Rotated object detection in aerial images is still challenging due to
arbitrary orientations, large scale and aspect ratio variations, and extreme
density of objects. Existing state-of-the-art rotated object detection methods
mainly rely on angle-based detectors. However, angle regression can easily
suffer from the long-standing boundary problem. To tackle this problem, we
propose a purely angle-free framework for rotated object detection, called
Point RCNN, which mainly consists of PointRPN and PointReg. In particular,
PointRPN generates accurate rotated RoIs (RRoIs) by converting the learned
representative points with a coarse-to-fine manner, which is motivated by
RepPoints. Based on the learned RRoIs, PointReg performs corner points
refinement for more accurate detection. In addition, aerial images are often
severely unbalanced in categories, and existing methods almost ignore this
issue. In this paper, we also experimentally verify that re-sampling the images
of the rare categories will stabilize training and further improve the
detection performance. Experiments demonstrate that our Point RCNN achieves the
new state-of-the-art detection performance on commonly used aerial datasets,
including DOTA-v1.0, DOTA-v1.5, and HRSC2016.
