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Automated Cargo Inspection: Exploring the use of Machine Vision in X-ray Transmission Imaging

22 March 2013


Thomas Rogers


X-ray transmission imaging has been widely deployed around the world to detect potential threats and other contraband in large containers, vehicles and even people. It offers a non-destructive method of inspecting goods in transportation systems and is quickly becoming mandatory. For example, the Department of Homeland Security’s Container Security Initiative aims to scan 100% of cargo containers entering the US. However, the current technique requires the manual inspection of X-ray images, which is resource intensive, time consuming and potentially prone to human error. Therefore, this proposal makes steps towards trying to automate the image inspection stage.  Dual energy X-ray imaging allows for the separation of materials (e.g. plastics from metals) in a cargo image, which may help in the automatic detection of threats. However, two problems (known as boom wobble and direction effect) may arise that can degrade the quality of material separation. The first part of this proposal will attempt to solve these problems and to hence improve material separation. The second part of this proposal is specifically aimed at the detection of counterfeit cigarettes. The detection of counterfeit cigarettes is ideal for preliminary studies of using machine vision to detect illicit goods in X-ray images. Counterfeit cigarettes are becoming increasingly prevalent across the EU. For example, one study showed that 30.9% of cigarettes in Birmingham (UK) where counterfeit and that the total number had doubled during a one year period. Moreover, the UK Border Agency has intercepted cigarettes containing asbestos, mould and human excrement. So counterfeit cigarettes can pose serious health risks to the consumer. At the same time, cigarettes should be relatively simple to detect because they are uniform and cuboidal in structure and they have periodic features in an X-ray image.  This project is funded by Rapiscan Systems and EPSRC.