Deep Learning in X-ray Testing

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Domingo Mery, Christian Pieringer


Deep learning has been inspired by ideas from neuroscience. The key idea of deep learning is to replace handcrafted features (explained in details in Chap. 5) with features that are learned efficiently using a hierarchical feature extraction approach. Usually, the learned features are so discriminative that no sophisticated classifiers are required. In last years, deep learning has been successfully used in image and video recognition, and it has been established as the state of the art in many areas such as computer vision, machine translation, and natural language processing. In comparison with other computer vision applications, we have seen that the introduction of techniques based on deep learning in computer vision for X-ray testing has been rather slow. However, there are many methods based on deep learning that have been designed and tested in some X-ray testing applications. In this chapter, we review many relevant concepts of deep learning that can be used in computer vision for X-ray testing. We covered the theory and practice of deep learning techniques in real X-ray testing problems. The chapter explained neural networks, Convolutional Neural Network (CNN) that can be used in classification problems, pre-trained models, transfer learning that are used in sophisticated models, Generative Adversarial Networks (GANs) to generate synthetic images, and modern detection methods that are used to classify and localize objects in an image. In addition, for every method, we give not only the basic concepts but also practical details in real X-ray testing examples that have been implemented in Python.

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