Instance Segmentation XXL-CT Challenge of a Historic Airplane
Abstract Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentation labels, limited applicable segmentation tools, as well as partially degraded image quality. To asses recent advancements in the field of machine learning-based image segmentation, the ‘Instance Segmentation XXL-CT Challenge of a Historic Airplane’ was conducted. The challenge aimed to explore automatic or interactive instance segmentation methods for an efficient delineation of the different aircraft components, such as screws, rivets, metal sheets or pressure tubes. We report the organization and outcome of this challenge and describe the capabilities and limitations of the submitted segmentation methods.
Authors
- Thomas C. Wittenberg (ORCID: https://orcid.org/0000-0003-0840-8695)
- Stefan Gerth (ORCID: https://orcid.org/0000-0002-5590-2920)
- Markus Michen
- Maik Stille (ORCID: https://orcid.org/0009-0009-7451-0779)
- Roland Gruber (ORCID: https://orcid.org/0000-0002-3429-732X)
- Nele Blum (ORCID: https://orcid.org/0009-0008-2622-181X)
- Johann Christopher Engster (ORCID: https://orcid.org/0009-0006-1966-7851)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Fraunhofer Institute for Integrated Circuits (DE)
- Fraunhofer-Einrichtung für Individualisierte Medizintechnik (DE)
- University of Lübeck (DE)
Publication Details
- Journal
- Journal of Nondestructive Evaluation
- Published
- 2024-11-05
- DOI
- https://doi.org/10.1007/s10921-024-01136-y
- Primary Topic
- 3D Surveying and Cultural Heritage
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- ADA Lovelace Center for Analytics, Data, Applications
- Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie