機械学習による外観検査のためのGANと切り貼り手法を組合せた画像生成に関する検討

抄録

This study proposes a novel data augmentation method to improve the accuracy of appearance inspection by machine learning, by combining GANs and the cut-and-paste method. When employing this method and training the model with the augmented dataset, the defect detection ratio improved by 2.0pt compared to the case without augmentation. Moreover, the system developed in this study enables automatic annotation of each training data, unlike the conventional supervised learning method that requires manual annotation for each data. This automated system effectively reduces training setup time, human workload, and human variability.