  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.
