Industrial segment anything - A case study in aircraft manufacturing, intralogistics, maintenance, repair, and overhaul

Moenck, Keno; Wendt, Arne; Prünte, Philipp Julian; Koch, Julian; Sahrhage, Arne; Gierecker, Johann; Schmedemann, Ole; Kähler, Falko; Holst, Dirk; Gomse, Martin; Schüppstuhl, Thorsten; Schoepflin, Daniel

doi:10.15480/882.8510

Industrial segment anything - A case study in aircraft manufacturing, intralogistics, maintenance, repair, and overhaul

Citation Link: https://doi.org/10.15480/882.8510

Start Page

1

End Page

30

Citation

arXiv: 2307.12674 (2023)

Publisher DOI

10.48550/arXiv.2307.12674

Peer Reviewed

false

Deploying deep learning-based applications in specialized domains like the aircraft production industry typically suffers from the training data availability problem. Only a few datasets represent non-everyday objects, situations, and tasks. Recent advantages in research around Vision Foundation Models (VFM) opened a new area of tasks and models with high generalization capabilities in non-semantic and semantic predictions. As recently demonstrated by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a promising direction in tackling the boundaries spanned by data, context, and sensor variety. Although, investigating its application within specific domains is subject to ongoing research. This paper contributes here by surveying applications of the SAM in aircraft production-specific use cases. We include manufacturing, intralogistics, as well as maintenance, repair, and overhaul processes, also representing a variety of other neighboring industrial domains. Besides presenting the various use cases, we further discuss the injection of domain knowledge.