Application of machine learning to x-ray diffraction-based classification

Bi Zhao; Scott Wolter; Joel A. Greenberg

doi:10.1117/12.2304683

4 May 2018 Application of machine learning to x-ray diffraction-based classification

Bi Zhao, Scott Wolter, Joel A. Greenberg

Proceedings Volume 10632, Anomaly Detection and Imaging with X-Rays (ADIX) III; 1063205 (2018) https://doi.org/10.1117/12.2304683
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

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.

Conference Presentation

Citation Download Citation

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 (4 May 2018); https://doi.org/10.1117/12.2304683

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