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Automated detection of smuggled high-risk security threats using Deep Learning

Jaccard, N; Rogers, TW; Morton, EJ; Griffin, LD; (2018) Automated detection of smuggled high-risk security threats using Deep Learning. In: Proceedings of the 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016). IEEE: Madrid, Spain. Green open access

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Abstract

The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called "Small Metallic Threats" (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in stream-of-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image).

Type: Proceedings paper
Title: Automated detection of smuggled high-risk security threats using Deep Learning
Event: 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016)
Open access status: An open access version is available from UCL Discovery
DOI: 10.1049/ic.2016.0079
Publisher version: https://doi.org/10.1049/ic.2016.0079
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Deep Learning , X-ray , Small Metallic Threats , Border Security
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10062497
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