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.

翻译：深度学习的兴起为图像处理领域带来了变革性时代，特别是在计算机断层扫描（CT）领域。深度学习对工业CT领域做出了重要贡献。然而，许多缺陷检测算法直接应用于重建域，往往忽略了原始传感器数据。本文将焦点转向正弦图的应用。在此框架下，我们提出了一种完整的三步深度学习算法，旨在无需图像重建即可识别和分析物体内部的缺陷。这三步分别是：缺陷分割、掩膜隔离和缺陷分析。我们采用基于U-Net架构进行缺陷分割。在模拟数据上，我们的方法实现了92.02%的交并比，在512像素宽探测器上进行缺陷检测时，平均位置误差为1.3像素。