20.500.12556/RUL-158239 Active learning and novel model calibration measurements for automated visual inspection in manufacturing 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. active learning smart manufacturing probability calibration artificial intelligence machine learning automated visual inspection pametna proizvodnja nadzor proizvodnje vizualni nadzor zagotavljanje kakovosti proizvodnja aktivno učenje umetna inteligenca strojno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2024-05-30 14:56:01 2024-05-30 14:56:03 2024-06-18 03:48:36 0000-00-00 00:00:00 2024 0 0 Str. 1963–1984 iss. 5 Vol. 35 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 658.5 1572-8145 10.1007/s10845-023-02098-0 181386243 s10845-023-02098-0.pdf s10845-023-02098-0.pdf 1 54DD3FF34971CD8A459B2AC072909F40 f73c00e59614658ac03dbaa31a1946ee138f937cc572a13ae26a7761d675b256 62c20d24-1e84-11ef-b1e4-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=185759 https://link.springer.com/article/10.1007/s10845-023-02098-0 1 faa3cb84-1e83-11ef-b1e4-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=185758 Fakulteta za elektrotehniko 0 0 0