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

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007548
URN to cite this document: urn:nbn:de:bvb:703-epub-7548-0

Title data

Xiong, Felix ; Kühl, Niklas ; Stauder, Maximilian:
Designing a computer-vision-based artifact for automated quality control : a case study in the food industry.
In: Flexible Services and Manufacturing Journal. Vol. 36 (2024) . - pp. 1422-1449.
ISSN 1936-6590
DOI der Verlagsversion: https://doi.org/10.1007/s10696-023-09523-9

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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. Deep-learning-based and traditional computer vision approaches are both integrated and benchmarked against each other. Through the measurement of a variety of performance metrics, valuable insights and key learnings for future adoptions of the framework are derived.

Further data

Item Type: Article in a journal
Keywords: Computer vision; Quality control; DCAI; Deep learning; Packaging
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Informatics and Human-Centered Artificial Intelligence > Chair Business Informatics and Human-Centered Artificial Intelligence - Univ.-Prof. Dr.-Ing. Niklas Kühl
Faculties
Faculties > Faculty of Law, Business and Economics
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Informatics and Human-Centered Artificial Intelligence
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-7548-0
Date Deposited: 14 Mar 2024 06:19
Last Modified: 06 Dec 2024 09:13
URI: https://epub.uni-bayreuth.de/id/eprint/7548

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