提出新模型提升雷达图像中多尺度密集船只检测精度
Enhancing, Refining, and Fusing: Towards Robust Multi-Scale and Dense Ship Detection
- 用旋转卷积强化船体中心特征，抑制背景干扰
- 通过跨层关联细化密集场景下船体边界，提升定位准确率
- 融合浅层与深层特征，增强多尺度目标检测能力
合成孔径雷达（SAR）成像因具备高分辨率、全天候及昼夜工作能力，在海上应用中至关重要。然而，SAR图像中的船只检测面临复杂背景、密集排列目标及大尺度变化等挑战。为此，我们提出一种新型框架——中心感知SAR船只检测器（CASS-Det），针对鲁棒的多尺度和密集船只检测设计。CASS-Det包含三项关键创新：(1) 中心增强模块（CEM）采用旋转卷积突出船体中心，改善定位并抑制背景干扰；(2) 邻居注意力模块（NAM）利用跨层依赖关系，精炼密集场景下的船体边界；(3) 跨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表明，CASS-Det在多尺度和密集排列船只检测上达到当前最优性能。
原文摘要 · Abstract (English)
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.
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