Détail du document
Identifiant
oai:arXiv.org:2402.11996
Sujet
Computer Science - Computer Vision... Computer Science - Machine Learnin...Auteur
Kozlovsky, Shir Joglekar, Omkar Di Castro, DotanCatégorie
Computer Science
Année
2024
Date de référencement
06/03/2024
Mots clés
computer segmentation instanceRésumé
In the field of robotics and automation, conventional object recognition and instance segmentation methods face a formidable challenge when it comes to perceiving Deformable Linear Objects (DLOs) like wires, cables, and flexible tubes.
This challenge arises primarily from the lack of distinct attributes such as shape, color, and texture, which calls for tailored solutions to achieve precise identification.
In this work, we propose a foundation model-based DLO instance segmentation technique that is text-promptable and user-friendly.
Specifically, our approach combines the text-conditioned semantic segmentation capabilities of CLIPSeg model with the zero-shot generalization capabilities of Segment Anything Model (SAM).
We show that our method exceeds SOTA performance on DLO instance segmentation, achieving a mIoU of $91.21\%$.
We also introduce a rich and diverse DLO-specific dataset for instance segmentation.
Kozlovsky, Shir,Joglekar, Omkar,Di Castro, Dotan, 2024, ISCUTE: Instance Segmentation of Cables Using Text Embedding
