Tackling the x-ray cargo inspection challenge using machine learning

Nicolas Jaccard; Thomas W. Rogers; Edward J. Morton; Lewis D. Griffin

doi:10.1117/12.2222765

12 May 2016 Tackling the x-ray cargo inspection challenge using machine learning

Nicolas Jaccard, Thomas W. Rogers, Edward J. Morton, Lewis D. Griffin

Proceedings Volume 9847, Anomaly Detection and Imaging with X-Rays (ADIX); 98470N (2016) https://doi.org/10.1117/12.2222765
Event: SPIE Defense + Security, 2016, Baltimore, MD, 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.

Citation Download 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 (12 May 2016); https://doi.org/10.1117/12.2222765

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