@inproceedings{e48054850ca44d1cb18d883b7d6744e7,
title = "Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes",
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.",
keywords = "Deep Learning, Quality Control, Transfer Learning, Data augmentation, X-Ray, Anomaly Detection, Convolutional Neural Network",
author = "Jensen, {Simon Buus} and Moeslund, {Thomas B.} and Andreasen, {S{\o}ren J.}",
year = "2022",
month = feb,
day = "5",
doi = "10.5220/0010785400003124",
language = "English",
volume = "4",
series = "International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications",
publisher = "SCITEPRESS Digital Library",
pages = "323--330",
booktitle = "Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 4: VISAPP",
note = "17th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP, GRAPP ; Conference date: 06-02-2022 Through 09-02-2022",
}