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In recent years, there have been ongoing efforts to improve screeningtechnologies to improve security and prevent terrorist threats. The most widelyused technologies for scanning shipping containers are gamma and x-rayradiography, which can be harmful to operators and the environment. Muontomography screening systems are considered as a potential tool to enhanceborder security and prevent terrorist threats or smuggling, especially in thecontext of shipping container inspections. Muon tomography is a technique thatuses naturally occurring cosmic ray muons to create detailed images of theinside of objects, such as shipping containers, without the need for physicalintervention. In this paper, we describe the algorithms that allow automaticdetection of illegal dangerous items hidden inside legal cargo in shippingcontainers. We used the Point of Closest Approach (PoCA) reconstructionalgorithm to reconstruct the 3D image of the shipping container and applied thenearest neighbor filtering method to separate and differentiate the filamentmaterials from the complex structure of the surrounding common materials. Theuse of these methods makes it possible to identify implicit information in thedata and visualize the contents of transport containers with preciselocalization of threats or contraband materials.

Automated object detection for muon tomography data analysis