  Detection of unwanted (`foreign') objects within products is a common
procedure in many branches of industry for maintaining production quality.
X-ray imaging is a fast, non-invasive and widely applicable method for foreign
object detection. Deep learning has recently emerged as a powerful approach for
recognizing patterns in radiographs (i.e., X-ray images), enabling automated
X-ray based foreign object detection. However, these methods require a large
number of training examples and manual annotation of these examples is a
subjective and laborious task. In this work, we propose a Computed Tomography
(CT) based method for producing training data for supervised learning of
foreign object detection, with minimal labour requirements. In our approach, a
few representative objects are CT scanned and reconstructed in 3D. The
radiographs that have been acquired as part of the CT-scan data serve as input
for the machine learning method. High-quality ground truth locations of the
foreign objects are obtained through accurate 3D reconstructions and
segmentations. Using these segmented volumes, corresponding 2D segmentations
are obtained by creating virtual projections. We outline the benefits of
objectively and reproducibly generating training data in this way compared to
conventional radiograph annotation. In addition, we show how the accuracy
depends on the number of objects used for the CT reconstructions. The results
show that in this workflow generally only a relatively small number of
representative objects (i.e., fewer than 10) are needed to achieve adequate
detection performance in an industrial setting. Moreover, for real experimental
data we show that the workflow leads to higher foreign object detection
accuracies than with standard radiograph annotation.
