Automatic Image Generation Pipeline for Instance Segmentation of Deformable Linear Objects.

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 validatio

SUBMITTER: Dirr J 

PROVIDER: S-EPMC10058460 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature