Enhancing, Refining, and Fusing: Towards Robust Multi-Scale and Dense Ship Detection

TL;DR

This paper introduces CASS-Det, a novel SAR ship detection framework that enhances localization, boundary refinement, and multi-scale feature fusion to improve detection accuracy in complex maritime scenes.

Contribution

The paper presents a new SAR ship detection framework with three innovative modules for center emphasis, boundary refinement, and multi-scale feature fusion, advancing robustness and accuracy.

Findings

Achieves state-of-the-art detection performance on multiple datasets.

Effectively detects multi-scale and densely packed ships.

Outperforms existing methods in complex maritime scenarios.

Abstract

Synthetic aperture radar (SAR) imaging, celebrated for its high resolution, all-weather capability, and day-night operability, is indispensable for maritime applications. However, ship detection in SAR imagery faces significant challenges, including complex backgrounds, densely arranged targets, and large scale variations. To address these issues, we propose a novel framework, Center-Aware SAR Ship Detector (CASS-Det), designed for robust multi-scale and densely packed ship detection. CASS-Det integrates three key innovations: (1) a center enhancement module (CEM) that employs rotational convolution to emphasize ship centers, improving localization while suppressing background interference; (2) a neighbor attention module (NAM) that leverages cross-layer dependencies to refine ship boundaries in densely populated scenes; and (3) a cross-connected feature pyramid network (CC-FPN) that enhances multi-scale feature fusion by integrating shallow and deep features. Extensive experiments on the SSDD, HRSID, and LS-SSDD-v1.0 datasets demonstrate the state-of-the-art performance of CASS-Det, excelling at detecting multi-scale and densely arranged ships.