KeyShip: Towards High-Precision Oriented SAR Ship Detection Using Key Points

Abstract

Synthetic Aperture Radar (SAR) is an all-weather sensing technology that has proven its effectiveness for ship detection. However, detecting ships accurately with oriented bounding boxes (OBB) on SAR images is challenging due to arbitrary ship orientations and misleading scattering. In this article, we propose a novel anchor-free key-point-based detection method, KeyShip, for detecting orientated SAR ships with high precision. Our approach uses a shape descriptor to model a ship as a combination of three types of key points located at the short-edge centers, long-edge centers, and the target center. These key points are detected separately and clustered based on predicted shape descriptors to construct the final OBB detection results. To address the boundary problem that arises with the shape descriptor representation, we propose a soft training target assignment strategy that facilitates successful shape descriptor training and implicitly learns the shape information of the targets. Our experimental results on three datasets (SSDD, RSDD, and HRSC2016) demonstrate our proposed method’s high performance and robustness.

Keywords

• oriented object detection
• ship detection
• synthetic aperture radar (SAR)
• key point detection
• shape descriptor