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Authors: Simon B. Jensen 1 ; Thomas B. Moeslund 1 and Søren J. Andreasen 2

Affiliations: 1 Department of Architecture and Media Technology, Aalborg University, Aalborg, Denmark ; 2 Serenergy, Aalborg, Denmark

Keyword(s): Anomaly Detection, Deep Learning, Convolutional Neural Network, X-Ray, Data Augmentation, Transfer Learning, Quality Control.

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 electrod es 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. (More)

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Paper citation in several formats:
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; ISSN 2184-4321, SciTePress, pages 323-330. DOI: 10.5220/0010785400003124

@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},
issn={2184-4321},
}

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
IS - 2184-4321
AU - Jensen, S.
AU - Moeslund, T.
AU - Andreasen, S.
PY - 2022
SP - 323
EP - 330
DO - 10.5220/0010785400003124
PB - SciTePress