Active Learning for Automated Visual Inspection of Manufactured Products
This work addresses quality control automation for manufacturing enterprises, presenting an incremental improvement in efficiency.
The study tackled the problem of reducing data labeling effort in automated visual defect inspection for manufactured products, showing that active learning approaches maintain model performance while cutting labeling costs.
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.