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 this https URL and is also
integrated in our open source rotation detection benchmark:
this https URL

本文提出了一种基于分类的旋转检测方法，采用两种密集编码标签替换了传统稀疏编码标签，配合损失重加权的策略，有效地提高了分类检测器的训练速度和检测准确率，适用于航拍图像、场景文本和面部等多种视觉应用。

密集标签编码用于无边界不连续旋转检测

Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

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。