  Quality control is a key activity performed by manufacturing enterprises to
ensure products meet quality standards and avoid potential damage to the
brand's reputation. The decreased cost of sensors and connectivity enabled an
increasing digitalization of manufacturing. In addition, artificial
intelligence enables higher degrees of automation, reducing overall costs and
time required for defect inspection. In this research, we compare three active
learning approaches and five machine learning algorithms applied to visual
defect inspection with real-world data provided by Philips Consumer Lifestyle
BV. Our results show that active learning reduces the data labeling effort
without detriment to the models' performance.
