  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).
