Arbitrary-Oriented Ellipse Detector for Ship Detection in Remote Sensing Images

Abstract

At present, in arbitrary-oriented object detection, the angular periodicity problem of rotated bounding box described by angle causes an object to have different numerical representations, which leads to uncertainty of rotated bounding box regression. To eliminate the angular periodicity problem, in this paper, we propose a novel and simple ellipse parameters representation method for arbitrary-oriented object, which hides the angle of object in the focal vector of ellipse to avoid direct angle prediction. Moreover, the proposed representation method can enable the arbitrary-oriented object to have only one numerical representation, which is beneficial to alleviate the uncertainty of bounding box regression. In order to adapt the proposed ellipse parameters representation method, we adopt 2D Gaussian distribution label assign for coarse samples selection, then the KLD loss and SimOTA are used to refine the coarse samples to obtain the best positive samples. We extend the YOLOX with medium parameters as an oriented ship detector according to the proposed ellipse parameters representation method, and conduct the experiments on HRSC2016, RSDD-SAR and RHRSID to demonstrate the effectiveness of the proposed method. The experimental results show that the proposed representation method achieves impressive results compared with state-of-the-art arbitrary-oriented object detection methods.