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

Expert Systems with Applications, vol. 206, pp. 117768

Abstract

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 labor requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that are 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. 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.

Authors 6

1. Mathé T. Zeegers corresponding

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

2. Tristan van Leeuwen

   Centrum Wiskunde & Informatica · Utrecht University

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

   Mathematical Institute, Utrecht University, Budapestlaan 6, 3584 CD Utrecht, The Netherlands

3. Daniël M. Pelt Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

   Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands

4. Sophia Bethany Coban

   Centrum Wiskunde & Informatica · University of Manchester

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

   Department of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom

5. Robert van Liere

   Centrum Wiskunde & Informatica · Eindhoven University of Technology

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

   Faculteit Wiskunde en Informatica, Technical University Eindhoven, Groene Loper 5, 5612 AZ Eindhoven, The Netherlands

6. Kees Joost Batenburg Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

   Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands