论文标题
基于学习的X射线图像燃料电池电极的X射线图像
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes
论文作者
论文摘要
在过去的几十年中，X射线图像中的异常检测一直是一个活跃而持久的研究领域，尤其是在医疗X射线图像的领域。对于这项工作，我们创建了一个标有异常数据集的现实世界，由涂有铂催化剂溶液涂有燃料电池电极的16位X射线图像数据组成，并使用深度学习方法在数据集上进行异常检测。该数据集包含一组不同的异常，其中有11个常见异常，其中电极包含例如划痕，气泡，污垢等。我们尝试使用16位图像到8位图像转换方法，以利用预训练的卷积神经网络作为特征提取器（转移学习），并发现我们通过在16位到8位对转换过程中，通过直率图均衡来实现最佳性能。我们将带有异常的燃料电池电极分为一个称为异常的单个类，将正常燃料电池电极分成一个称为正常的类，从而将异常检测问题抽象为二进制分类问题。我们达到85.18 \％的平衡精度。公司Serenergy使用了异常检测，以优化燃料电池电极质量控制的时间
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
