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From Transparent Labware Segmentation to Collision Avoidance: A Real-Time Edge-Aware Perception Pipeline — arXiv:2608.04769

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.04769
License (as archived): CC0-1.0
Archived: 2026-10-31T11:50:04.291Z
SHA-256 of the archived text: 71974e996d4ac573d5da101bccbc27a8fecd5af236b94cdace158c591e55ae97
Stable URL of this page: https://metraipolis.com/documento/dc_582d63d00aac

How to cite

From Transparent Labware Segmentation to Collision Avoidance: A Real-Time Edge-Aware Perception Pipeline — arXiv:2608.04769 — arXiv (en), https://arxiv.org/abs/2608.04769 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_582d63d00aac (Metraipolis archive dc_582d63d00aac, sha256 71974e996d4ac573d5da101bccbc27a8fecd5af236b94cdace158c591e55ae97)

BibTeX

@misc{dc582d63d00aac,
  title = {From Transparent Labware Segmentation to Collision Avoidance: A Real-Time Edge-Aware Perception Pipeline — arXiv:2608.04769},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.04769},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_582d63d00aac; sha256 71974e996d4ac573d5da101bccbc27a8fecd5af236b94cdace158c591e55ae97},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.04769. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper presents an edge-aware instance segmentation framework that enables real-time robotic collision avoidance with transparent laboratory glassware using purely visual perception. Transparent vessels defy conventional segmentation due to refraction, specular reflection, and the absence of stable interior texture, yet their boundary contours remain comparatively reliable visual cues. Exploiting this observation, we augment a one-stage real-time instance segmentation backbone with a lightweight edge-detection branch, edge-guided attention fusion, and a parameter-free SimAM module, and further construct LabGlass-IS, a 3485-image, 21-category instance segmentation dataset of real laboratory glassware. The enhanced model achieves the highest Boundary F-score of 97.80 among compared methods, outperforming the YOLO-prompted FastSAM framework by 18.93 BF points. Furthermore, it maintains an inference speed of 7.1ms per frame and requires only 2.85% of the parameters of the closest accuracy competitor. Multi-view triangulation of mask centroids further provides 3D positions for conservative bounding-volume collision constraints. Real-robot trials achieve a 93.3% collision avoidance success rate, indicating the feasibility of the proposed perception-to-action pipeline for robot collision avoidance among fragile transparent objects. Our code is available at this https URL . Our video is available at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-06 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2608.04769 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T11:39:29.942Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-08-06?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).