Automated object detection for muon tomography data analysis

Abstract

In recent years, there have been ongoing efforts to improve screening technologies to improve security and prevent terrorist threats. The most widely used technologies for scanning shipping containers are gamma and x-ray radiography, which can be harmful to operators and the environment. Muon tomography screening systems are considered as a potential tool to enhance border security and prevent terrorist threats or smuggling, especially in the context of shipping container inspections. Muon tomography is a technique that uses naturally occurring cosmic ray muons to create detailed images of the inside of objects, such as shipping containers, without the need for physical intervention. In this paper, we describe the algorithms that allow automatic detection of illegal dangerous items hidden inside legal cargo in shipping containers. We used the Point of Closest Approach (PoCA) reconstruction algorithm to reconstruct the 3D image of the shipping container and applied the nearest neighbor filtering method to separate and differentiate the filament materials from the complex structure of the surrounding common materials. The use of these methods makes it possible to identify implicit information in the data and visualize the contents of transport containers with precise localization of threats or contraband materials.