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.
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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 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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