Generative adversarial networks for ultrasound image synthesis and analysis in nondestructive evaluation : doctoral thesis - Posilović, Luka (L. Posilović ; Faculty of electrical engineering and computing, 2022)
Non-destructive ultrasound evaluation is a technique for flaw detection in materials. Among other things, it is used for monitoring key components of nuclear power plants, railways, and pipelines. Analysis of ultrasound data is performed by human inspectors manually analyzing acquired images. Such a process can be tedious and is highly dependent on the inspector's previous experience. Deep learning methods are hard to develop because of the lack of available data. The shortage also impacts training new human experts in this field, since they learn through experience. Data from real inspections can not be used because of non-disclosure agreements. On the other hand, blocks with synthetic flaws are expensive to produce. The goal of this work is to develop methods for defect detection. To improve the method’s performance, deep learning methods for generating additional synthetic images need to be developed. Generated data should be realistic and of high quality even to human experts in ultrasound evaluation. Additional data should be used to improve the performance of the deep learning defect detector.