Streaming Machine Learning and Online Active Learning for Automated Visual Inspection
This work addresses automated quality control in manufacturing to reduce costs and bias, but it is incremental as it applies existing methods to a specific domain.
The study compared five streaming machine learning algorithms for visual defect inspection using real-world data from Philips, finding that active learning reduced data labeling effort by nearly 15% on average in the worst case while maintaining acceptable performance, and machine learning models could speed up quality inspection by up to 40%.
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided greater data availability. Such data availability has spurred the development of artificial intelligence models, which allow higher degrees of automation and reduced bias when inspecting the products. Furthermore, the increased speed of inspection reduces overall costs and time required for defect inspection. In this research, we compare five streaming machine learning algorithms applied to visual defect inspection with real-world data provided by Philips Consumer Lifestyle BV. Furthermore, we compare them in a streaming active learning context, which reduces the data labeling effort in a real-world context. Our results show that active learning reduces the data labeling effort by almost 15% on average for the worst case, while keeping an acceptable classification performance. The use of machine learning models for automated visual inspection are expected to speed up the quality inspection up to 40%.