增强、精炼与融合：迈向鲁棒的多尺度与密集船舶检测
Enhancing, Refining, and Fusing: Towards Robust Multi-Scale and Dense Ship Detection
AI总结：
针对SAR图像中复杂背景、密集目标和尺度变化问题，提出CASS-Det框架，通过CEM、NAM和CC-FPN增强中心、精炼边界并融合多尺度特征，在三个数据集上达到最先进检测性能。
摘要：
合成孔径雷达（SAR）成像以其高分辨率、全天候能力和昼夜可操作性而著称，对海事应用不可或缺。然而，SAR图像中的船舶检测面临重大挑战，包括复杂背景、密集排列的目标以及巨大的尺度变化。为解决这些问题，我们提出了一种新颖框架 Center-Aware SAR Ship Detector（CASS-Det），专为鲁棒的多尺度和密集排布船舶检测而设计。CASS-Det集成了三项关键创新：（1）中心增强模块（CEM），采用旋转卷积突出船舶中心，在抑制背景干扰的同时改善定位；（2）邻居注意力模块（NAM），利用跨层依赖关系精炼密集场景中的船舶边界；（3）交叉连接特征金字塔网络（CC-FPN），通过整合浅层与深层特征增强多尺度特征融合。在SSDD、HRSID和LS-SSDD-v1.0数据集上的大量实验证明了CASS-Det的最先进性能，其在检测多尺度和密集排列船舶方面表现出色。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
