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12 May 2016 Tackling the x-ray cargo inspection challenge using machine learning
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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.
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 (12 May 2016);

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