# A tomographic workflow to enable deep learning for X-ray based foreign object detection

arXiv: https://arxiv.org/abs/2201.12184 · HTML: https://mlsanity.com/paper/2201.12184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 83 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 25 of 25)

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- [A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks](https://mlsanity.com/paper/2010.00421/) (2020)
- [A Multi-channel DART Algorithm](https://mlsanity.com/paper/1808.09170/) (2018)
- [Unsupervised foreign object detection based on dual-energy absorptiometry in the food industry](https://mlsanity.com/paper/2104.05326/) (2021)

## Abstract (arXiv)

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.