Automatic Image Generation Pipeline for Instance Segmentation of Deformable Linear Objects

Jonas Dirr, Daniel Gebauer, Jiajun Yao, Rüdiger Daub

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Robust detection of deformable linear objects (DLOs) is a crucial challenge for the automation of handling and assembly of cables and hoses. The lack of training data is a limiting factor for deep-learning-based detection of DLOs. In this context, we propose an automatic image generation pipeline for instance segmentation of DLOs. In this pipeline, a user can set boundary conditions to generate training data for industrial applications automatically. A comparison of different replication types of DLOs shows that modeling DLOs as rigid bodies with versatile deformations is most effective. Further, reference scenarios for the arrangement of DLOs are defined to generate scenes in a simulation automatically. This allows the pipelines to be quickly transferred to new applications. The validation of models trained with synthetic images and tested on real-world images shows the feasibility of the proposed data generation approach for segmentation of DLOs. Finally, we show that the pipeline yields results comparable to the state of the art but has advantages in reduced manual effort and transferability to new use cases.

Original languageEnglish
Article number3013
JournalSensors (Switzerland)
Volume23
Issue number6
DOIs
StatePublished - Mar 2023

Keywords

  • cable
  • data-centric AI
  • deformable one-dimensional objects
  • domain randomization
  • machine vision
  • synthetic images
  • wire

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