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Quality control is a crucial activity performed by manufacturing enterprisesto ensure that their products meet quality standards and avoid potential damageto the brand's reputation. The decreased cost of sensors and connectivityenabled increasing digitalization of manufacturing. In addition, artificialintelligence enables higher degrees of automation, reducing overall costs andtime required for defect inspection. This research compares three activelearning approaches, having single and multiple oracles, to visual inspection.Six new metrics are proposed to assess the quality of calibration without theneed for ground truth. Furthermore, this research explores whether existingcalibrators can improve their performance by leveraging an approximate groundtruth to enlarge the calibration set. The experiments were performed onreal-world data provided by Philips Consumer Lifestyle BV. Our results showthat the explored active learning settings can reduce the data labeling effortby between three and four percent without detriment to the overall qualitygoals, considering a threshold of p=0.95. Furthermore, the results show thatthe proposed calibration metrics successfully capture relevant informationotherwise 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 obtainground truth data.

Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing