  Arbitrary-oriented objects exist widely in natural scenes, and thus the
oriented object detection has received extensive attention in recent years. The
mainstream rotation detectors use oriented bounding boxes (OBB) or
quadrilateral bounding boxes (QBB) to represent the rotating objects. However,
these methods suffer from the representation ambiguity for oriented object
definition, which leads to suboptimal regression optimization and the
inconsistency between the loss metric and the localization accuracy of the
predictions. In this paper, we propose a Representation Invariance Loss (RIL)
to optimize the bounding box regression for the rotating objects. Specifically,
RIL treats multiple representations of an oriented object as multiple
equivalent local minima, and hence transforms bounding box regression into an
adaptive matching process with these local minima. Then, the Hungarian matching
algorithm is adopted to obtain the optimal regression strategy. We also propose
a normalized rotation loss to alleviate the weak correlation between different
variables and their unbalanced loss contribution in OBB representation.
Extensive experiments on remote sensing datasets and scene text datasets show
that our method achieves consistent and substantial improvement. The source
code and trained models are available at https://github.com/ming71/RIDet.
