Industrial Segment Anything -- a Case Study in Aircraft Manufacturing,
  Intralogistics, Maintenance, Repair, and Overhaul
Keno Moenck Arne Wendt Philipp Prünte Julian Koch Arne Sahrhage Johann Gierecker Ole Schmedemann Falko Kähler Dirk Holst Martin Gomse Thorsten Schüppstuhl Daniel Schoepflin
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.