Streaming Machine Learning and Online Active Learning for Automated Visual Inspection

TL;DR

This paper evaluates streaming machine learning algorithms for automated visual defect inspection, demonstrating that active learning reduces labeling effort and speeds up quality control processes in manufacturing.

Contribution

It compares five streaming ML algorithms and introduces active learning to reduce labeling effort in real-world visual inspection tasks.

Findings

Active learning reduces labeling effort by nearly 15%.

Machine learning speeds up inspection by up to 40%.

Active learning maintains acceptable classification performance.

Abstract

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%.