# ISCUTE: Instance Segmentation of Cables Using Text Embedding

Source: [ISCUTE: Instance Segmentation of Cables Using Text Embedding](<https://www.sota2.com/research/paper/68fa725d1e209131cea55faf>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:24:11.561Z

Authors: Shir Kozlovsky, Omkar Joglekar, Dotan Di Castro

Published: 2024-02-19T09:41:57.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2402.11996>)

[PDF](<https://arxiv.org/pdf/2402.11996.pdf>)