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

Author

Listed:
  • Felix Xiong

    (Karlsruhe Institute of Technology)

  • Niklas Kühl

    (Karlsruhe Institute of Technology
    University of Bayreuth)

  • Maximilian Stauder

    (Schwarz IT KG)

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.

Suggested Citation

  • Felix Xiong & Niklas Kühl & Maximilian Stauder, 2024. "Designing a computer-vision-based artifact for automated quality control: a case study in the food industry," Flexible Services and Manufacturing Journal, Springer, vol. 36(4), pages 1422-1449, December.
    • Handle: RePEc:spr:flsman:v:36:y:2024:i:4:d:10.1007_s10696-023-09523-9
    DOI: 10.1007/s10696-023-09523-9
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    References listed on IDEAS

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    1. Mona Haji & Laoucine Kerbache & Mahaboob Muhammad & Tareq Al-Ansari, 2020. "Roles of Technology in Improving Perishable Food Supply Chains," Logistics, MDPI, vol. 4(4), pages 1-24, December.
    2. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    3. Igor Popovic & Bart A. G. Bossink & Peter C. van der Sijde, 2019. "Factors Influencing Consumers’ Decision to Purchase Food in Environmentally Friendly Packaging: What Do We Know and Where Do We Go from Here?," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    4. repec:dar:wpaper:135656 is not listed on IDEAS
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