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

Author:

Ge Junyao1ORCID,Tang Yiping1,Guo Kaitai1ORCID,Zheng Yang1ORCID,Hu Haihong1,Liang Jimin1ORCID

Affiliation:

1. School of Electronic Engineering, Xidian University, Xi’an 710071, China

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.

Funder

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. LPST-Det: Local-Perception-Enhanced Swin Transformer for SAR Ship Detection;Remote Sensing;2024-01-26

2. An Anchor-Free Method Based on Transformers and Adaptive Features for Arbitrarily Oriented Ship Detection in SAR Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

3. SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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