A Demonstrator for Threat Detection in Volumetric CT Scans

Abstract

The detection of threat items and prohibited items inside closed containers is an active field of research and an even more important real-world application. While conventional systems use X-ray projections for inspection, they lack true three-dimensional information which is crucial for a reliable detection. In this work, we introduce a demonstrator setup for three-dimensional threat detection within a 3D printed miniature container. We created a data processing pipeline that automatically integrates CT scanning, volumetric reconstruction and the actual detection of threat objects by means of a neural network. The achieved results are very promising and could be obtained using very few annotated training samples which is a realistic assumption in this application.