Sensory Inspection of Wood Grain Images Using Disentangled Representation Learning

Abstract

This study aims to automate the sensory inspection of the wood grain. Sensory inspections rely on human perception, which requires considerable experience to master. Additionally, verbalizing and quantifying the inspection indices makes it difficult to unify the evaluation among inspectors. To overcome these challenges, we propose a method that models the cognitive mechanisms of skilled inspectors to automate sensory inspections and clarify potential factors of sensibility. We confirmed that our proposed method is more effective for the sensory inspection of wood grain images than conventional anomaly detection methods. Moreover, our experiments demonstrated the potential usefulness of our proposed method in the field of wood quality control.