# Deformable One-Dimensional Object Detection for Routing and Manipulation

arXiv: https://arxiv.org/abs/2201.06775 · HTML: https://mlsanity.com/paper/2201.06775/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Distance Transform for Tubular Structure Segmentation in CT Scans](https://mlsanity.com/paper/1912.03383/) (2019)
- [Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects](https://mlsanity.com/paper/1911.06283/) (2019)
- [Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints](https://mlsanity.com/paper/2011.00627/) (2020)
- [Real-Time Ellipse Detection for Robotics Applications](https://mlsanity.com/paper/2102.12670/) (2021)

## Built on by (most cited first, 5 of 5)

- [Efficient Spatial Representation and Routing of Deformable One-Dimensional Objects for Manipulation](https://mlsanity.com/paper/2202.06172/) (2022)
- [A Systematic Literature Review of Computer Vision Applications in Robotized Wire Harness Assembly](https://mlsanity.com/paper/2309.13744/) (2023)
- [Detection and Physical Interaction with Deformable Linear Objects](https://mlsanity.com/paper/2205.08041/) (2022)
- [ISCUTE: Instance Segmentation of Cables Using Text Embedding](https://mlsanity.com/paper/2402.11996/) (2024)
- [Self-Supervised Learning for Interactive Perception of Surgical Thread for Autonomous Suture Tail-Shortening](https://mlsanity.com/paper/2307.06845/) (2023)

## Abstract (arXiv)

Many methods exist to model and track deformable one-dimensional objects (e.g., cables, ropes, and threads) across a stream of video frames. However, these methods depend on the existence of some initial conditions. To the best of our knowledge, the topic of detection methods that can extract those initial conditions in non-trivial situations has hardly been addressed. The lack of detection methods limits the use of the tracking methods in real-world applications and is a bottleneck for fully autonomous applications that work with these objects. This paper proposes an approach for detecting deformable one-dimensional objects which can handle crossings and occlusions. It can be used for tasks such as routing and manipulation and automatically provides the initialization required by the tracking methods. Our algorithm takes an image containing a deformable object and outputs a chain of fixed-length cylindrical segments connected with passive spherical joints. The chain follows the natural behavior of the deformable object and fills the gaps and occlusions in the original image. Our tests and experiments have shown that the method can correctly detect deformable one-dimensional objects in various complex conditions.