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.
Kaitai Guo
Assistant Professor
My research interests include broad-spectrum substance identification, microwave and infrared imaging, and system simulation and evaluation.
Yang Zheng
Assistant Professor
My research interests include human behaviour analysis for intelligent diagnosis of developmental coordination disorder, aritifical intelligence, and computer vision.
Haihong Hu
Associate Professor
My research interests include signal and image processing and computer vision.
Jimin Liang
Professor of Electronic Engineering
My research interests include artificial intelligence and computer vision.