发表机构
University of Waterloo; Soongsil University（滑铁卢大学; 崇实大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机影像通信塔部件零样本分割的背景干扰问题，提出显著性-深度前景条件策略，集成至Grounded-SAM与SAM 3，在TOW-300数据集上提升了实例分割性能并减少误报。
AI 中文摘要
对无人机（UAV）影像中通信塔部件进行细粒度分割是自动化巡检的关键，但因实例级标注有限，难以开发任务特定模型。零样本分割模型是有前景的替代方案，但在杂乱场景中，视觉相似的背景结构会干扰部件定位，导致漏检和误报。我们提出一种与模型无关的显著性-深度前景条件策略，结合基于外观的显著性与单目相对深度，构建粗略的塔先验以抑制无关内容。我们将该模块与Grounded-SAM、SAM 3集成，得到SD-Grounded-SAM和SD-SAM 3。SD-Grounded-SAM在生成掩码前进一步应用几何与深度感知的框优化，而SD-SAM 3依赖SAM 3的内部设置。在含340张通信塔无人机影像的TOW-300数据集上，我们的策略提升了两个基线：SD-SAM 3实现了最强的实例分割性能，SD-Grounded-SAM则产生更少的误报。 ablation实验证实了显著性、深度和框优化的互补增益，提升了杂乱场景中的鲁棒性。
英文摘要
Fine-grained segmentation of communication-tower components in UAV imagery is essential for automated inspection, yet task-specific models are hard to develop due to limited instance-level annotations. Zero-shot segmentation models offer a promising alternative, but in cluttered scenes, visually similar background structures interfere with component localization, causing missed instances and false positives. We propose a model-agnostic saliency-depth foreground-conditioning strategy combining appearance-based saliency with monocular relative depth to construct a coarse tower prior and suppress irrelevant content. We integrate this module with Grounded-SAM and SAM 3, yielding SD-Grounded-SAM and SD-SAM 3. SD-Grounded-SAM further applies geometric and depth-aware box refinement before mask generation, while SD-SAM 3 relies on SAM 3's internal setup. On TOW-300, a dataset of 340 communication-tower UAV images, our strategy improves both baselines: SD-SAM 3 achieves the strongest instance-segmentation performance, while SD-Grounded-SAM produces fewer false positives. Ablations confirm complementary gains from saliency, depth, and box refinement, improving robustness in cluttered scenes.
