Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset
Abstract
In recent years, the industrial sector has evolved towards its fourth revolution. The quality control domain is particularly interested in advanced machine learning for computer vision anomaly detection. Nevertheless, several challenges have to be faced, including imbalanced datasets, the image complexity, and the zero-false-negative (ZFN) constraint to guarantee the high-quality requirement. This paper illustrates a use case for an industrial partner, where Printed Circuit Board Assembly (PCBA) images are first reconstructed with a Vector Quantized Generative Adversarial Network (VQGAN) trained on normal products. Then, several multi-level metrics are extracted on a few normal and abnormal images, highlighting anomalies through reconstruction differences. Finally, a classifer is trained to build a composite anomaly score thanks to the metrics extracted. This three-step approach is performed on the public MVTec-AD datasets and on the partner PCBA dataset, where it achieves a regular accuracy of 95.69% and 87.93% under the ZFN constraint.
Keywords
Cite
@article{arxiv.2211.15513,
title = {Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset},
author = {Arnaud Bougaham and Mohammed El Adoui and Isabelle Linden and Benoît Frénay},
journal= {arXiv preprint arXiv:2211.15513},
year = {2023}
}
Comments
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10994-023-06415-9