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 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