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28 July 2023 f-AnoGAN for non-destructive testing in industrial anomaly detection
Oumaima Sliti, Maxime Devanne, Sophie Kohler, Naim Samet, Jonathan Weber, Christophe Cudel
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Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 1274915 (2023) https://doi.org/10.1117/12.3000063
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France
Abstract
Being able to identify defects is an essential step during manufacturing processes. Yet, not all defects are necessarily known and sufficiently well described in the databases images. The challenge we address in this paper is to detect any defect by fitting a model using only normal samples of industrial parts. For this purpose, we propose to test fast AnoGAN (f-AnoGAN) approach based on a generative adversarial network (GAN). The method is an unsupervised learning algorithm, that contains two phases; first, we train a generative model using only normal images, which proposes a fast mapping of new data into the latent space. Second, we add and train an encoder to reconstruct images. The anomaly detection is defined by the reconstruction error between the defected data and the reconstructed ones, and the residual error of the discriminator. For our experiments, we use two sets of industrial data; the MVTec Anomaly Detection Dataset and a private dataset which is based on thermal-wave and used for non-destructive testing. This technique has been utilized in research for the evaluation of industrial materials. Applying the f-AnoGAN in this domain offers high anomaly detection accuracy.
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Oumaima Sliti, Maxime Devanne, Sophie Kohler, Naim Samet, Jonathan Weber, and Christophe Cudel "f-AnoGAN for non-destructive testing in industrial anomaly detection", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 1274915 (28 July 2023); https://doi.org/10.1117/12.3000063
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KEYWORDS
Education and training
Gallium nitride
Data modeling
Image restoration
Manufacturing
RGB color model
Statistical analysis
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