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.

本文提出了一种将角度预测任务从回归问题转变为分类问题的方法，并使用设计的 “圆滑标签” 和 “密集编码标签” 来解决周期性和编码长度过大的问题，在三个大规模公共数据集上进行了实验验证，证明了该方法的有效性。