Learning Visual Quality Inspection from Multiple Humans Using Ensembles of Classifiers

摘要

Visual quality inspection systems nowadays require the highest possible flexibility. Therefore, the reality that multiple human operators may be training the system has to be taken into account. This paper provides an analysis of this problem and presents a framework which is able to learn from multiple humans. This approach has important advantages over systems which are unable to do so, such as a consistent level of quality of the products, the ability to give operator-specific feedback, the ability to capture the knowledge of every operator separately and an easier training of the system. The level of contradiction between the decisions of the operators is assessed for data obtained from a real-world industrial system for visual quality inspection of the printing of labels on CDs, which was labelled separately by five different operators. The results of the experiments show that the system is able to resolve many of the contradictions which are present in the data. Furthermore, it is shown that in several cases the system even performs better than a classifier which is trained on the data provided by the supervisor itself.