用检测框引导大模型实现雷达图像零样本船只实例分割
Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery
- 用YOLOv11检测船体位置，生成框提示SAM2分割
- 零标注下达89.2%检出率，平均交并比0.637
- 无需微调，适合缺乏标注的遥感场景
合成孔径雷达（SAR）在海上监视中至关重要，但其深度学习分析受限于像素级标注的缺乏。本文探索通用视觉基础模型在SAR图像中实现零样本船只实例分割的可行性，无需像素级监督。基于YOLOv11的检测器在开放SAR数据集上训练，通过边界框定位船只，进而提示分割任意模型2（SAM2）生成实例掩码，无需任何掩码标注。与以往依赖微调或适配器的SAM方法不同，本方法仅依靠经SAR训练的检测器提供的空间约束，即可有效规范基础模型预测，部分缓解光学与SAR之间的域差距。该设计支持船只分类、尺寸估计和尾迹分析等下游应用。在SSDD基准测试中，平均交并比达0.637（为全监督基线的89%），整体船只检测率达89.2%，验证了基于基础模型的可扩展、低标注需求的SAR图像理解路径。
原文摘要 · Abstract (English)
Synthetic Aperture Radar (SAR) plays a critical role in maritime surveillance, yet deep learning for SAR analysis is limited by the lack of pixel-level annotations. This paper explores how general-purpose vision foundation models can enable zero-shot ship instance segmentation in SAR imagery, eliminating the need for pixel-level supervision. A YOLOv11-based detector trained on open SAR datasets localizes ships via bounding boxes, which then prompt the Segment Anything Model 2 (SAM2) to produce instance masks without any mask annotations. Unlike prior SAM-based SAR approaches that rely on fine tuning or adapters, our method demonstrates that spatial constraints from a SAR-trained detector alone can effectively regularize foundation model predictions. This design partially mitigates the optical-SAR domain gap and enables downstream applications such as vessel classification, size estimation, and wake analysis. Experiments on the SSDD benchmark achieve a mean IoU of 0.637 (89% of a fully supervised baseline) with an overall ship detection rate of 89.2%, confirming a scalable, annotation-efficient pathway toward foundation-model-driven SAR image understanding.
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