Defect detectability analysis via probability of defect detection between traditional and deep learning methods in numerical simulations

X-ray computed tomography (XCT) is one of the most powerful imaging techniques in non-destructive testing (NDT) for detecting, analysing and visualising defects such as pores, fibres, cracks etc. in industrial specimens. Detecting defects in X-ray images, however, is still a challenging problem, as it strongly depends on the quality of the XCT images. Numerical XCT simulation proved to be valuable in order to increase both image quality and detection performance. In this work, we thus analyse the differences between traditional segmentation techniques (i.e., k-means, watershed, Otsu thresholding) and deep learning-based methods (i.e., U-Net, V-Net, modified 3D U-Net) in terms of their defect detection capacity using virtual XCT images. For this purpose, we apply the probability of defect detection (POD) approach on simulated X-ray computed tomography data from aluminium cylinder heads. The XCT simulation tool SimCT was used to generate X-ray radiographs and respective reconstructions from a specimen series which features different well-defined defects with varying sizes, shapes and locations. To generate POD curves and to specify detection limits, the segmentation algorithms are used in predefined regions for defect detection via a hit/miss approach. A comparison and visualisation of six different types of defects is illustrated in 2D and 3D images, together with their POD curves and detection limits.