Active learning and novel model calibration measurements for automated visual inspection in manufacturing

Jože M. Rožanec (), Luka Bizjak (), Elena Trajkova (), Patrik Zajec (), Jelle Keizer (), Blaž Fortuna () and Dunja Mladenić ()
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Jože M. Rožanec: Jožef Stefan International Postgraduate School
Luka Bizjak: Jožef Stefan Institute
Elena Trajkova: University of Ljubljana
Patrik Zajec: Jožef Stefan Institute
Jelle Keizer: Philips Consumer Lifestyle BV
Blaž Fortuna: Qlector d.o.o.
Dunja Mladenić: Jožef Stefan Institute

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 5, No 5, 1963-1984

Abstract: Abstract Quality control is a crucial activity performed by manufacturing enterprises to ensure that their products meet quality standards and avoid potential damage to the brand’s reputation. The decreased cost of sensors and connectivity enabled increasing digitalization of manufacturing. In addition, artificial intelligence enables higher degrees of automation, reducing overall costs and time required for defect inspection. This research compares three active learning approaches, having single and multiple oracles, to visual inspection. Six new metrics are proposed to assess the quality of calibration without the need for ground truth. Furthermore, this research explores whether existing calibrators can improve performance by leveraging an approximate ground truth to enlarge the calibration set. The experiments were performed on real-world data provided by Philips Consumer Lifestyle BV. Our results show that the explored active learning settings can reduce the data labeling effort by between three and four percent without detriment to the overall quality goals, considering a threshold of p = 0.95. Furthermore, the results show that the proposed calibration metrics successfully capture relevant information otherwise available to metrics used up to date only through ground truth data. Therefore, the proposed metrics can be used to estimate the quality of models’ probability calibration without committing to a labeling effort to obtain ground truth data.

Keywords: Active learning; Probability calibration; Artificial intelligence; Machine learning; Smart manufacturing; Automated visual inspection (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02098-0

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