Deep Learning-based Anomaly Detection on X-Ray Images of Fuel Cell Electrodes

Simon B. Jensen, Thomas B. Moeslund, Søren J. Andreasen

2022

Abstract

Anomaly detection in X-ray images has been an active and lasting research area in the last decades, especially in the domain of medical X-ray images. For this work, we created a real-world labeled anomaly dataset, consisting of 16-bit X-ray image data of fuel cell electrodes coated with a platinum catalyst solution and perform anomaly detection on the dataset using a deep learning approach. The dataset contains a diverse set of anomalies with 11 identified common anomalies where the electrodes contain e.g., scratches, bubbles, smudges etc. We experiment with 16-bit image to 8-bit image conversion methods to utilize pre-trained Convolutional Neural Networks as feature extractors (transfer learning) and find that we achieve the best performance by maximizing the contrasts globally across the dataset during the 16-bit to 8-bit conversion, through histogram equalization. We group the fuel cell electrodes with anomalies into a single class called abnormal and the normal fuel cell electrodes into a class called normal, thereby abstracting the anomaly detection problem into a binary classification problem. We achieve a balanced accuracy of 85.18%. The anomaly detection is used by the company, Serenergy, for optimizing the time spend on the quality control of the fuel cell electrodes.

Paper Citation

in Harvard Style

Jensen S., Moeslund T. and Andreasen S. (2022). Deep Learning-based Anomaly Detection on X-Ray Images of Fuel Cell Electrodes. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 323-330. DOI: 10.5220/0010785400003124

in Bibtex Style

@conference{visapp22,
author={Simon B. Jensen and Thomas B. Moeslund and Søren J. Andreasen},
title={Deep Learning-based Anomaly Detection on X-Ray Images of Fuel Cell Electrodes},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={323-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010785400003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Deep Learning-based Anomaly Detection on X-Ray Images of Fuel Cell Electrodes
SN - 978-989-758-555-5
AU - Jensen S.
AU - Moeslund T.
AU - Andreasen S.
PY - 2022
SP - 323
EP - 330
DO - 10.5220/0010785400003124
PB - SciTePress