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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 <jats:italic>Philips Consumer Lifestyle BV<\/jats:italic>. 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 <jats:italic>p<\/jats:italic> = 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\u2019 probability calibration without committing to a labeling effort to obtain ground truth data.<\/jats:p>","DOI":"10.1007\/s10845-023-02098-0","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T16:03:36Z","timestamp":1678982616000},"page":"1963-1984","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Active learning and novel model calibration measurements for automated visual inspection in manufacturing"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3665-639X","authenticated-orcid":false,"given":"Jo\u017ee M.","family":"Ro\u017eanec","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luka","family":"Bizjak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Trajkova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrik","family":"Zajec","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jelle","family":"Keizer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bla\u017e","family":"Fortuna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dunja","family":"Mladeni\u0107","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"issue":"7","key":"2098_CR1","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1557\/mrs.2019.157","volume":"44","author":"KS Aggour","year":"2019","unstructured":"Aggour, K. 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