Enhancing, Refining, and Fusing: Towards Robust Multiscale and Dense Ship Detection

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 multiscale 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 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 multiscale feature fusion by integrating shallow and deep features. The proposed model achieves mean Average Precision of 99.2%, 93.1%, and 82.1% on the SSDD, HRSID, and LS-SSDD datasets, surpassing the second-best methods by 1.2%, 1.8%, and 1.8%, respectively, which demonstrates its effectiveness.