  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.
