engleski

Generative Adversarial Networks for Ultrasound Image Synthesis and Analysis in Nondestructive Evaluation

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. Although some methods for defect detection and classification were developed, they are mostly based on traditional image and signal processing techniques such as magnitude threshold. Such an approach does not yield satisfactory results and can not generalize well on data from various ultrasound probes and scanned materials. In the field of generating synthetic ultrasound data, yet much has to be done. Only some traditional and Finite Element Methods (FEM) have been proposed. All this means that there is much space for improvements and new ideas in the field of defect detection in ultrasound non-destructive evaluation. Guided by this need for improvements, in this thesis, several methods for non-destructive ultrasound data analysis and ultrasound image generation are proposed. These methods can be summarized as follows: * Generative adversarial network with additional object detector discriminator for synthesis of high quality realistic ultrasound images * Improved one-stage defect detector trained using additional ultrasound images synthesised using generative adversarial network * Method for anomaly detection in ultrasound images. Experimental results of the proposed methods are presented and discussed. The proposed generative method outperforms existing methods when the quality of images is assessed by human experts. The generated images can even be used to improve the performance of the state-of-the-art deep learning defect detector. Also, the analysis of different methods for anomaly detection in ultrasonic images is given alongside the improvements made to some methods.

non-destructive testing ; ultrasound image analysis ; automated flaw detection ; image augmentation ; image generation ; deep learning ; generative networks

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