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Proceedings Article

Tackling the x-ray cargo inspection challenge using machine learning

[+] Author Affiliations
Nicolas Jaccard, Thomas W. Rogers, Lewis D. Griffin

Univ. College London (United Kingdom)

Edward J. Morton

Rapiscan Systems Ltd. (United Kingdom)

Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470N (May 12, 2016); doi:10.1117/12.2222765
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From Conference Volume 9847

  • Anomaly Detection and Imaging with X-Rays (ADIX)
  • Amit Ashok; Mark A. Neifeld; Michael E. Gehm
  • Baltimore, Maryland, United States | April 17, 2016

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. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Nicolas Jaccard ; Thomas W. Rogers ; Edward J. Morton and Lewis D. Griffin
" Tackling the x-ray cargo inspection challenge using machine learning ", Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470N (May 12, 2016); doi:10.1117/12.2222765; http://dx.doi.org/10.1117/12.2222765


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