ISCUTE: Instance Segmentation of Cables Using Text Embedding
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 30 of its 44 references that are in the map. First posted on arXiv 19 Feb 2024.
Builds on
30 of 44 references.
References · 30
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18 more
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Citing papers per year: 2026: 2.
2 papers.