12 May 2016 Tackling the x-ray cargo inspection challenge using machine learning
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Proceedings Volume 9847, Anomaly Detection and Imaging with X-Rays (ADIX); 98470N (2016); doi: 10.1117/12.2222765
Event: SPIE Defense + Security, 2016, Baltimore, Maryland, United States
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.
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Nicolas Jaccard, Thomas W. Rogers, Edward J. Morton, 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 (12 May 2016); doi: 10.1117/12.2222765; http://dx.doi.org/10.1117/12.2222765
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KEYWORDS
X-rays

X-ray imaging

Inspection

System on a chip

Machine learning

Image segmentation

Visualization

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