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.
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