Aviation security, mail inspection, medical diagnostics and many other industries all face the same challenge: to accurately identify the presence of a target material concealed within a cluttered surrounding environment. X-ray systems that combine transmission and diffraction measurements promise excellent detection performance with low false alarm rates; however, conventional approaches to combining these measurements typically under-utilize the available information and result in higher overall system resource costs. Here, we consider a fully integrated approach to hybrid X-ray transmission and diffraction systems and discuss simulation- and experimental-based investigations of the design and performance (both imaging and detection) of such systems. Based on this analysis, we describe a hybrid system capable of scanning boxes and/or luggage and report its ability to distinguish materials of interest to aviation security and pharmaceutical inspection.
Transmission x-ray systems rely on the measured photon attenuation coefficients for material imaging and classification. While this approach provides high quality imaging capabilities and satisfactory object discrimination in most situations, it lacks material-specific information. For airport security, this can be a significant issue as false alarms require additional time to be resolved by human operators, which impacts bag throughput and airport operations. Orthogonal techniques such as X-ray Diffraction Tomography (XRDT) using a coded aperture provide complementary chemical/molecular signatures that can be used to identify a target material. The combination of noisy signals, variability in the XRD form factors for the same material, and the lack of a comprehensive material library limits the classification performance of the correlation based methods. Using simulated data to train a 1D Convolution Neural Network (CNN), we found relative improvements in classification accuracy compared to the correlation based approach we used previously. These improvement gains were cross-validated using the simulated data, and provided satisfactory detection results against real experimental data collected on a laboratory prototype.
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