Deep Learning Approach for Multi-Class Segmentation in Industrial CT-Data

Abstract

In this work, a multiclass segmentation of defects on industrial CT data is performed. The segmentation is implemented by using artificial neural networks (ANNs). Different architectures are evaluated and compared to find the best model architecture for this task. Different metrics for segmentation and classification analysis are used to evaluate the networks. The three best models are presented in this paper. All of them obtained positive results; the model with the U-Net architecture achieved the best results with a remarkable segmentation performance of 90.18% and a classification performance of 95.77%.