Inverse Mathematics Enhanced Neural Networks to Improve Defect Detection on Radiation Detectors

In this thesis, Convolutional Neural Networks (CNN) and Inverse Mathematic methods will be discussed for automated defect detection in materials that are used for radiation detectors. The first part of the thesis is dedicated to the literature review on the methods that are used. These include a general overview of Neural Networks, computer vision algorithms and Inverse Mathematics methods, such as wavelet transformations, or total variation denoising. In the Materials and Methods section, how these methods can be utilized in this problem setting will be examined. Results and Discussions part will reveal the outcomes and takeaways from the experiments. A focus of this thesis is put on the CNN architecture that fits the task best, how to optimize that chosen CNN architecture and discuss, how selected inputs created by Inverse Mathematics influence the Neural Network and it's performance. The results of this research reveal that the initially chosen Retina-Net is well suited for the task and the Inverse Mathematics methods utilized in this thesis provided useful insights