GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case

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Résumé

Industry 4.0 and recent deep learning progress make it possible to solve problems that traditional methods could not. This is the case for anomaly detection that received a particular attention from the machine learning community, and resulted in a use of generative adversarial networks (GANs). In this work, we propose to use intermediate patches for the inference step, after a WGAN training procedure suitable for highly imbalanced datasets, to make the anomaly detection possible on full size Printed Circuit Board Assembly (PCBA) images. We therefore show that our technique can be used to support or replace actual industrial image processing algorithms, as well as to avoid a waste of time for industries.
langue originaleAnglais
Pages1-14
Nombre de pages14
Etat de la publicationPublié - 29 sept. 2021
EvénementThird International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021. - Virtual (formerly Bilbao-Spain), Bilbao, Espagne
Durée: 17 sept. 202117 sept. 2021
https://lidta.dcc.fc.up.pt/

Atelier de travail

Atelier de travailThird International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021.
Titre abrégéLIDTA 2021
Pays/TerritoireEspagne
La villeBilbao
période17/09/2117/09/21
Adresse Internet

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