Industrial anomaly detection and one-class classification using generative adversarial networks

Y. T.K. Lai, Jwu-Sheng Hu, Y. H. Tsai, W. Y. Chiu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Industrial image datasets for quality inspection are mostly sparse in defects. It is then hard for both automated optical inspection (AOI) machines and simple neural network classifiers to inspect all defects effectively. In this work, we develop a novel framework for industrial anomaly detection in one-class classification manner, which utilized pre-trained generative adversarial networks (GANs) as the rule of thumb to perform anomaly detection. Our results show that GANs are able to capture arbitrary and structural industrial images and can effectively discern defects when the query images are defective.

Original languageEnglish
Title of host publicationAIM 2018 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1444-1449
Number of pages6
ISBN (Print)9781538618547
DOIs
StatePublished - 30 Aug 2018
Event2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2018 - Auckland, New Zealand
Duration: 9 Jul 201812 Jul 2018

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume2018-July

Conference

Conference2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2018
CountryNew Zealand
CityAuckland
Period9/07/1812/07/18

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  • Cite this

    Lai, Y. T. K., Hu, J-S., Tsai, Y. H., & Chiu, W. Y. (2018). Industrial anomaly detection and one-class classification using generative adversarial networks. In AIM 2018 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics (pp. 1444-1449). [8452228] (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIM.2018.8452228