GANの潜在空間探索を用いた木目画像の官能検査

抄録

This study addresses a problem in the sensory inspection of wood products. Since wooden products have large variations in color and pattern, it is challenging to learn the distribution of good products using conventional anomaly detection methods. In addition, the boundary between good and bad products in the sensory inspection of wooden products is ambiguous and difficult to discriminate correctly since the decision criteria depend heavily on the inspector's sensitivity. Therefore, we propose a novel method to determine the decision boundary according to sensibility obtained from GAN's latent space traversal and sensibility surveys. We found that the proposed method is more effective than conventional methods for detecting abnormalities in the sensory inspection of wood grain images.