不変情報クラスタリングと良品画像のみを用いて訓練された欠陥検知用CNNの提案

抄録

This paper proposes CNN training using only invariant information clustering (IIC) and non-defective images for visual inspection of industrial products. In the field of visual inspection, there is a problem that there are fewer defective images than non- defective images. To solve this problem, CNN is trained as an encoder using invariant information clustering, and only non-defective images. One-Class Support Vector Machine (OCSVM) was utilized to classify the feature vectors obtained from the encoder. Defective images and non-defective images are classified by detecting values that deviate from the features of the non-defective images. A typical supervised learning model, a model using a variational autoencoder (VAE), and an IIC model are compared. The results confirm that the IIC model that does not use defective images is equivalent to a supervised learning model that uses a small number of defective images.