论文标题
关于任意面向的对象检测：基于分类的方法重新审视
On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited
论文作者
论文摘要
任意面向对象检测是旋转敏感任务的基础。我们首先表明，根据参数化协议，在现有的基于回归的旋转探测器中遭受的边界问题是由角度周期性或角订购引起的。我们还表明，根本原因是理想的预测可以超出定义的范围。因此，我们将角度预测任务从回归问题转变为分类问题。对于所得的圆形分布角度分类问题，我们首先设计了一种圆形平滑标签技术来处理角度的周期性并增加对相邻角度的误差耐性。为了通过圆形平滑标签减少过多的模型参数，我们进一步设计了一个密集编码的标签，从而大大降低了编码的长度。最后，我们进一步开发了一个对象标题检测模块，当需要确切的标题方向信息，例如用于船舶和飞机标题检测。我们发布了我们的OHD-SJTU数据集和OHDET检测器以进行标题检测。关于航空图像的三个大型公共数据集的广泛实验结果，即Dota，HRSC2016，OHD-SJTU和FACE DATASET FDDB，以及场景文本数据集ICDAR2015和MLT，显示了我们方法的有效性。
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.
