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Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks

Engineering and Technology

Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks

T. Partridge, A. Astolfo, et al.

This groundbreaking research by T. Partridge, A. Astolfo, S. S. Shankar, F. A. Vittoria, M. Endrizzi, S. Arridge, T. Riley-Smith, I. G. Haig, D. Bate, and A. Olivo reveals how combining dark-field x-ray imaging with deep neural networks significantly enhances the detection of threat materials. The study showcases proof-of-concept experiments that showcase remarkable improvements in material identification, promising advancements in security technologies.

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Playback language: English
Abstract
This paper explores the enhanced detection of threat materials using a combination of dark-field x-ray imaging and deep neural networks. Dark-field imaging, sensitive to material inhomogeneities, creates characteristic textures. Combining this with conventional attenuation improves threat material discrimination. The energy dependence of these signals further refines identification. Two proof-of-concept studies demonstrate the effectiveness of deep neural networks in identifying materials from dark-field textures, significantly outperforming methods without dark-field data. While further research is needed, the results highlight the potential for this combined approach in security applications.
Publisher
Nature Communications
Published On
Sep 09, 2022
Authors
T. Partridge, A. Astolfo, S. S. Shankar, F. A. Vittoria, M. Endrizzi, S. Arridge, T. Riley-Smith, I. G. Haig, D. Bate, A. Olivo
Tags
dark-field imaging
x-ray imaging
deep neural networks
threat material detection
material identification
security applications
image analysis
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