X-ray imaging is widely used for non-destructive detection of defects in industrial products on a conveyor belt. Real-time detection requires highly accurate, robust, and fast algorithms to analyze X-ray images. Deep convolutional neural networks (DCNNs) satisfy these requirements if a large amount of labeled data is available. To overcome the challenge of collecting these data, different methods of X-ray image generation can be considered. Depending on the desired level of similarity to real data, various physical effects either should be simulated or can be ignored. X-ray scattering is known to be computationally expensive to simulate, and this effect can heavily influence the accuracy of a generated X-ray image. We propose a methodology for quantitative evaluation of the effect of scattering on defect detection. This methodology compares the accuracy of DCNNs trained on different versions of the same data that include and exclude the scattering signal. We use the Probability of Detection (POD) curves to find the size of the smallest defect that can be detected with a DCNN and evaluate how this size is affected by the choice of training data. We apply the proposed methodology to a model problem of defect detection in cylinders. Our results show that the exclusion of the scattering signal from the training data has the largest effect on the smallest detectable defects. Furthermore, we demonstrate that accurate inspection is more reliant on high-quality training data for images with a high quantity of scattering. We discuss how the presented methodology can be used for other tasks and objects.

翻译：X射线成像广泛应用于传送带上工业产品缺陷的非破坏性检测。实时检测需要高精度、高鲁棒性且快速的算法来分析X射线图像。当拥有大量标注数据时，深度卷积神经网络（DCNNs）能够满足这些要求。为克服数据采集难题，可考虑采用不同的X射线图像生成方法。根据与真实数据所需相似度的不同，某些物理效应需要模拟而另一些则可忽略。已知X射线散射的模拟计算成本较高，该效应会显著影响生成X射线图像的精度。我们提出一种定量评估散射对缺陷检测影响的方法论。该方法通过比较基于同一数据的不同版本（包含与排除散射信号）训练的DCNN的检测精度，并利用检测概率（POD）曲线确定DCNN可检测的最小缺陷尺寸，评估训练数据选择对该尺寸的影响。我们将所提方法论应用于圆柱体缺陷检测的模型问题。结果表明，从训练数据中排除散射信号对最小可检测缺陷的影响最为显著。此外，我们验证了对于散射量较大的图像，高精度检测更依赖于高质量训练数据。最后讨论了该方法论在其他任务与对象上的应用前景。