  Rotation detection serves as a fundamental building block in many visual
applications involving aerial image, scene text, and face etc. Differing from
the dominant regression-based approaches for orientation estimation, this paper
explores a relatively less-studied methodology based on classification. The
hope is to inherently dismiss the boundary discontinuity issue as encountered
by the regression-based detectors. We propose new techniques to push its
frontier in two aspects: i) new encoding mechanism: the design of two Densely
Coded Labels (DCL) for angle classification, to replace the Sparsely Coded
Label (SCL) in existing classification-based detectors, leading to three times
training speed increase as empirically observed across benchmarks, further with
notable improvement in detection accuracy; ii) loss re-weighting: we propose
Angle Distance and Aspect Ratio Sensitive Weighting (ADARSW), which improves
the detection accuracy especially for square-like objects, by making DCL-based
detectors sensitive to angular distance and object's aspect ratio. Extensive
experiments and visual analysis on large-scale public datasets for aerial
images i.e. DOTA, UCAS-AOD, HRSC2016, as well as scene text dataset ICDAR2015
and MLT, show the effectiveness of our approach. The source code is available
at https://github.com/Thinklab-SJTU/DCL_RetinaNet_Tensorflow and is also
integrated in our open source rotation detection benchmark:
https://github.com/yangxue0827/RotationDetection.
