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Mathé T. Zeegers

2022-01-14

A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection

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This submission contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included. The data is supplementary material to the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022].

Additional Metadata
Keywords X-ray imaging, Radiographs, Object detection, Machine learning, Deep learning, Foreign object
Persistent URL doi.org/10.5281/zenodo.5681008
Project Real-Time 3D Tomography
Rights creativecommons.org/licenses/by/4.0/legalcode
Note [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow enabling deep learning for X-ray based foreign object detection", 2022 (in preparation) [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.
Grant This work was funded by the The Netherlands Organisation for Scientific Research (NWO); grant id nwo/639.073.506 - Real-Time 3D Tomography
Organisation Computational Imaging
Citation
Mathé T. Zeegers. (2022). A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection. doi:10.5281/zenodo.5681008


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