  Arbitrary-oriented object detection has been a building block for rotation
sensitive tasks. We first show that the boundary problem suffered in existing
dominant regression-based rotation detectors, is caused by angular periodicity
or corner ordering, according to the parameterization protocol. We also show
that the root cause is that the ideal predictions can be out of the defined
range. Accordingly, we transform the angular prediction task from a regression
problem to a classification one. For the resulting circularly distributed angle
classification problem, we first devise a Circular Smooth Label technique to
handle the periodicity of angle and increase the error tolerance to adjacent
angles. To reduce the excessive model parameters by Circular Smooth Label, we
further design a Densely Coded Labels, which greatly reduces the length of the
encoding. Finally, we further develop an object heading detection module, which
can be useful when the exact heading orientation information is needed e.g. for
ship and plane heading detection. We release our OHD-SJTU dataset and OHDet
detector for heading detection. Extensive experimental results on three
large-scale public datasets for aerial images i.e. DOTA, HRSC2016, OHD-SJTU,
and face dataset FDDB, as well as scene text dataset ICDAR2015 and MLT, show
the effectiveness of our approach.
