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Tackling the X-ray cargo inspection challenge using machine learning

Jaccard, N; Rogers, TW; Morton, EJ; Griffin, LD; (2016) Tackling the X-ray cargo inspection challenge using machine learning. In: Ashok, A and Neifeld, MA and Gehm, ME, (eds.) Anomaly Detection and Imaging with X-Rays (ADIX), 98470N (May 12, 2016); doi:10.1117/12.2222765. (pp. 98470N). Society of Photo-Optical Instrumentation Engineers (SPIE): Bellingham, Washington, USA. Green open access

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Abstract

The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection.

Type: Proceedings paper
Title: Tackling the X-ray cargo inspection challenge using machine learning
Event: Anomaly Detection and Imaging with X-Rays (ADIX), 17 April 2016, Baltimore, Maryland, USA
Location: Baltimore, US
Dates: 17 April 2016 - 21 April 2016
ISBN-13: 9781510600881
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/12.2222765
Publisher version: http://dx.doi.org/10.1117/12.2222765
Language: English
Additional information: Copyright © (2016) Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Keywords: Cargo screening, X-ray imaging, Classification, Machine Learning, Deep Learning, Threat Image Projection, TIP
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1502135
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