  The rise of deep learning has introduced a transformative era in the field of
image processing, particularly in the context of computed tomography. Deep
learning has made a significant contribution to the field of industrial
Computed Tomography. However, many defect detection algorithms are applied
directly to the reconstructed domain, often disregarding the raw sensor data.
This paper shifts the focus to the use of sinograms. Within this framework, we
present a comprehensive three-step deep learning algorithm, designed to
identify and analyze defects within objects without resorting to image
reconstruction. These three steps are defect segmentation, mask isolation, and
defect analysis. We use a U-Net-based architecture for defect segmentation. Our
method achieves the Intersection over Union of 92.02% on our simulated data,
with an average position error of 1.3 pixels for defect detection on a
512-pixel-wide detector.
