精密工学会誌
Online ISSN : 1882-675X
Print ISSN : 0912-0289
ISSN-L : 0912-0289
画像技術の実利用特集論文
少数不良品サンプル下におけるAdversarial AutoEncoderによる正常モデルの生成と異常検出
中塚 俊介相澤 宏旭加藤 邦人
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2018 年 84 巻 12 号 p. 1071-1078

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For industrial products and foods, it is essential to conduct a visual inspection to improve the quality of products. In recent years, automation by a neural network has been considered but learning a neural networks requires a lot of good and defective samples. However it is so difficult to ensure a lot of defective samples that neural networks cannot learn properly. In this paper, we aimed at discrimination of defects under conditions where there is a large number of good products and a small number of defective products. By combining AAE, which can extract features following any distribution and Hotelling's T-Square, which is an effective anomaly detection method when data follows a normal distribution, it is possible to discriminate defects under a small number of defective samples. We experimented on 2 dataset and showed the effectiveness.

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