Problem
Research questions and friction points this paper is trying to address.
zero-shot segmentation
communication-tower components
UAV imagery
cluttered scenes
instance-level annotations
Saliency-Depth Conditioning for Zero-Shot Segmentation of Communication-Tower Components in Cluttered UAV Imagery
🤖 AI Summary
针对通信塔组件在杂乱无人机图像中的零样本分割问题，提出了一种结合显著性和单目相对深度的前景条件策略，提升了分割模型的性能。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
saliency-depth conditioning
zero-shot segmentation
UAV imagery
model-agnostic strategy
box refinement
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