  Common horizontal bounding box (HBB)-based methods are not capable of
accurately locating slender ship targets with arbitrary orientations in
synthetic aperture radar (SAR) images. Therefore, in recent years, methods
based on oriented bounding box (OBB) have gradually received attention from
researchers. However, most of the recently proposed deep learning-based methods
for OBB detection encounter the boundary discontinuity problem in angle or key
point regression. In order to alleviate this problem, researchers propose to
introduce some manually set parameters or extra network branches for
distinguishing the boundary cases, which make training more diffcult and lead
to performance degradation. In this paper, in order to solve the boundary
discontinuity problem in OBB regression, we propose to detect SAR ships by
learning polar encodings. The encoding scheme uses a group of vectors pointing
from the center of the ship target to the boundary points to represent an OBB.
The boundary discontinuity problem is avoided by training and inference
directly according to the polar encodings. In addition, we propose an Intersect
over Union (IOU) -weighted regression loss, which further guides the training
of polar encodings through the IOU metric and improves the detection
performance. Experiments on the Rotating SAR Ship Detection Dataset (RSSDD)
show that the proposed method can achieve better detection performance over
other comparison algorithms and other OBB encoding schemes, demonstrating the
effectiveness of our method.
