UAV画像の雑然シーンにおける通信鉄塔部品のゼロショットセグメンテーションのための顕著性-深度条件付け
Saliency-Depth Conditioning for Zero-Shot Segmentation of Communication-Tower Components in Cluttered UAV Imagery
UAV画像中の通信鉄塔部品を、顕著性と深度情報を用いて粗い塔の事前情報を構築し、Grounded-SAMやSAM 3と組み合わせることで、ゼロショットで高精度にセグメンテーションする手法を提案した。
著者: Ali Lesani, Chul Min Yeum, Su-Min Kang
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
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.