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arXiv Computer Vision By Ali Lesani, Chul Min Yeum, Su-Min Kang

Saliency-Depth Conditioning for Zero-Shot Segmentation of Communication-Tower Components in Cluttered UAV Imagery

Read the original on arXiv Computer Vision →

The paper introduces a saliency-depth conditioning approach for zero‑shot segmentation of communication‑tower components in cluttered UAV imagery. By combining appearance‑based saliency with monocular relative depth, the method creates a coarse tower prior that suppresses irrelevant background, and integrates this module with Grounded‑SAM and SAM 3 to produce SD‑Grounded‑SAM and SD‑SAM 3. Experiments on the TOW‑300 dataset show that SD‑SAM 3 achieves the best instance‑segmentation performance while SD‑Grounded‑SAM reduces false positives, with ablations confirming the complementary benefits of saliency, depth, and box refinement.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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