  Boundary discontinuity and its inconsistency to the final detection metric
have been the bottleneck for rotating detection regression loss design. In this
paper, we propose a novel regression loss based on Gaussian Wasserstein
distance as a fundamental approach to solve the problem. Specifically, the
rotated bounding box is converted to a 2-D Gaussian distribution, which enables
to approximate the indifferentiable rotational IoU induced loss by the Gaussian
Wasserstein distance (GWD) which can be learned efficiently by gradient
back-propagation. GWD can still be informative for learning even there is no
overlapping between two rotating bounding boxes which is often the case for
small object detection. Thanks to its three unique properties, GWD can also
elegantly solve the boundary discontinuity and square-like problem regardless
how the bounding box is defined. Experiments on five datasets using different
detectors show the effectiveness of our approach. Codes are available at
https://github.com/yangxue0827/RotationDetection and
https://github.com/open-mmlab/mmrotate.
