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

TL;DR

This paper presents a deep learning approach for anomaly detection in X-ray images of fuel cell electrodes, introducing a new labeled dataset and achieving high accuracy to assist quality control processes.

Contribution

The work introduces a real-world labeled dataset of fuel cell electrode X-ray images and demonstrates effective transfer learning techniques for anomaly detection.

Findings

Achieved 85.18% balanced accuracy in binary classification

Histogram equalization improves image contrast for better detection

Dataset includes diverse anomalies like scratches and bubbles

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