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Designing a computer-vision-based artifact for automated quality control: a case study in the food industry

Xiong, Felix 1; Kühl, Niklas ORCID iD icon 2; Stauder, Maximilian
1 Karlsruher Institut für Technologie (KIT)
2 Institut für Wirtschaftsinformatik und Marketing (IISM), Karlsruher Institut für Technologie (KIT)

Abstract:

Reducing waste through automated quality control (AQC) has both positive economical and ecological effects. In order to incorporate AQC in packaging, multiple quality factor types (visual, informational, etc.) of a packaged artifact need to be evaluated. Thus, this work proposes an end-to-end quality control framework evaluating multiple quality control factors of packaged artifacts (visual, informational, etc.) to enable future industrial and scientific use cases. The framework includes an AQC architecture blueprint as well as a computer vision-based model training pipeline. The framework is designed generically, and then implemented based on a real use case from the packaging industry. As an innovate approach to quality control solution development, the data-centric artificial-intelligence (DCAI) paradigm is incorporated in the framework. The implemented use case solution is finally tested on actual data. As a result, it is shown that the framework’s implementation through a real industry use case works seamlessly and achieves superior results. The majority of packaged artifacts are correctly classified with rapid prediction speed. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000167545
Veröffentlicht am 25.01.2024
Originalveröffentlichung
DOI: 10.1007/s10696-023-09523-9
Scopus
Zitationen: 1
Web of Science
Zitationen: 1
Dimensions
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2024
Sprache Englisch
Identifikator ISSN: 1936-6582, 1936-6590
KITopen-ID: 1000167545
Erschienen in Flexible Services and Manufacturing Journal
Verlag Springer-Verlag
Band 36
Heft 4
Seiten 1422–1449
Vorab online veröffentlicht am 04.01.2024
Schlagwörter Computer vision, Quality control, DCAI, Deep learning, Packaging
Nachgewiesen in Dimensions
Web of Science
Scopus
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