Summary of Iscute: Instance Segmentation Of Cables Using Text Embedding, by Shir Kozlovsky et al.
ISCUTE: Instance Segmentation of Cables Using Text Embedding
by Shir Kozlovsky, Omkar Joglekar, Dotan Di Castro
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to recognizing Deformable Linear Objects (DLOs) like wires, cables, and flexible tubes in robotics and automation settings. Traditional object recognition methods struggle with these objects due to the lack of distinct attributes such as shape, color, and texture. The authors combine two AI models, CLIPSeg and Segment Anything Model (SAM), to develop a text-promptable and user-friendly DLO instance segmentation technique that outperforms state-of-the-art (SOTA) methods. The proposed method achieves a mean Intersection-over-Union (mIoU) of 91.21%. Additionally, the paper introduces a new dataset for DLO instance segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how robots can better recognize flexible objects like wires and cables. Right now, robots have trouble identifying these kinds of things because they don’t have clear shapes or colors to look at. The authors created a special AI model that combines two other models to help robots identify these flexible objects more accurately. This new approach is very good at recognizing these objects, achieving a high score of 91.21%. The paper also provides a new dataset for testing this technology. |
Keywords
* Artificial intelligence * Instance segmentation * Sam
