論文
多重解像度マップを持つ補集合GANを用いた正常データのみの学習による外観検査法
キーワード:
visual inspection,
neural networks,
anomaly detection,
Generative Adversarial Networks,
multi scale
2021 年 87 巻 1 号 p. 120-126
詳細
2021 年 87 巻 1 号 p. 120-126
In the case of industrial products, visual inspection is essential to improve the quality of products. In recent years, automation with neural networks has been considered. However, the conventional model discriminating good or defective products requires a large amount of good and defective samples for learning. In fact, it is difficult to ensure a large amount of defective samples. Therefore, in this research, anomaly (=defective samples) are detected by modeling the normal distribution and its complement from only a large amount of good products. Since the defect is a part of the image and its size varies, we propose the structure of Multi-scale Patch Discriminator in this paper.
