论文标题
基于极坐标的遥感图像检测对象检测
Objects detection for remote sensing images based on polar coordinates
论文作者
论文摘要
任意面向对象检测是遥感对象检测字段中的重要任务。现有的研究表明，极性坐标系在处理旋转对象建模的问题方面具有明显的优势，也就是说，使用较少的参数来实现更准确的旋转对象检测。但是，基于深度学习的当前最先进的探测器都是在笛卡尔坐标中建模的。在本文中，我们首次向深度学习检测器介绍了极地坐标系，并提出了一个无锚的极性遥感对象检测器（P-RSDET），该检测器（P-RSDET）可以通过使用更简单的对象表示模型和更少的回归参数来实现竞争性检测准确性。在P-RSDET方法中，可以通过预测中心点并回归一个极性半径和两个极角来实现任意的对象检测。此外，为了表达极性半径和极角之间的几何约束关系，提出了极性环面积损耗函数以提高角位置的预测准确性。关于DOTA，UCAS-AOD和NWPU VHR-10数据集的实验表明，我们的P-RSDET可以使用更简单的模型和更少的回归参数来实现最先进的性能。
Arbitrary-oriented object detection is an important task in the field of remote sensing object detection. Existing studies have shown that the polar coordinate system has obvious advantages in dealing with the problem of rotating object modeling, that is, using fewer parameters to achieve more accurate rotating object detection. However, present state-of-the-art detectors based on deep learning are all modeled in Cartesian coordinates. In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Object Detector (P-RSDet), which can achieve competitive detection accuracy via uses simpler object representation model and less regression parameters. In P-RSDet method, arbitrary-oriented object detection can be achieved by predicting the center point and regressing one polar radius and two polar angles. Besides, in order to express the geometric constraint relationship between the polar radius and the polar angle, a Polar Ring Area Loss function is proposed to improve the prediction accuracy of the corner position. Experiments on DOTA, UCAS-AOD and NWPU VHR-10 datasets show that our P-RSDet achieves state-of-the-art performances with simpler model and less regression parameters.
