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Defective products detection using adversarial AutoEncoder
Author(s): Shunsuke Nakatsuka; Hiroaki Aizawa; Kunihito Kato
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Paper Abstract

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. Although automation of a visual inspection is essential to improve the quality of products, either or both of the features extracted by the experts and balanced dataset are needed. We tackled such a problem. 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 with a small number of defective samples.

Paper Details

Date Published: 22 March 2019
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110490U (22 March 2019); doi: 10.1117/12.2521371
Show Author Affiliations
Shunsuke Nakatsuka, Gifu Univ. (Japan)
Hiroaki Aizawa, Gifu Univ. (Japan)
Kunihito Kato, Gifu Univ. (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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