X-ray diffraction-based baggage screening provides the potential for the material sensitivity needed to realize high detection probabilities and low false alarm rates. However, the combination of noisy signals, variability in the XRD form factors based on slight material differences, and incomplete material libraries lead to decreased system performance. By using a machine learning classification approach to XRD-based explosives detection, we show that the probability of error can be reduced relative to traditional, correlation-based classifiers. This improved performance exists at a variety of noise levels and degrees of library completeness, and indicates a path toward increased XRD-based classifier robustness.
Bi Zhao, Scott Wolter, and Joel A. Greenberg, "Application of machine learning to x-ray diffraction-based classification," Proc. SPIE 10632, Anomaly Detection and Imaging with X-Rays (ADIX) III, 1063205 (Presented at SPIE Defense + Security: April 18, 2018; Published: 4 May 2018); https://doi.org/10.1117/12.2304683.
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