ORPSD: Outer Rectangular Projection-Based Representation for Oriented Ship Detection in SAR Images

Ship object detection in synthetic aperture radar (SAR) images is both an important and challenging task. Previous methods based on horizontal bounding boxes struggle to accurately locate densely packed ships oriented in arbitrary directions, due to variations in scale, aspect ratio, and orientation, thereby requiring other forms of object representation, like rotated bounding boxes (OBBs). However, most deep learning-based OBB detection methods share a single-stage paradigm to improve detection speed, often at the expense of accuracy. In this paper, we propose a simple yet effective two-stage detector dubbed ORPSD, which enjoys good accuracy and efficiency owing to two key designs. First, we design a novel encoding scheme based on outer-rectangle projection (ORP) for the OrpRPN stage, which could efficiently generate high-quality oriented proposals. Second, we propose a convex quadrilateral rectification (CQR) method to rectify distorted shape proposals into rectangles by finding the outer rectangle based on the minimum area, ensuring correct proposal orientation. Comparative experiments on the challenging public benchmarks RSSDD and RSAR demonstrate the superiority of our ORPDet over previous OBB-based detectors in terms of both detection accuracy and efficiency.