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

Published

Conference Paper

© Copyright 2018 SPIE. 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.

Full Text

Duke Authors

Cited Authors

  • Zhao, B; Wolter, S; Greenberg, JA

Published Date

  • January 1, 2018

Published In

Volume / Issue

  • 10632 /

Electronic International Standard Serial Number (EISSN)

  • 1996-756X

International Standard Serial Number (ISSN)

  • 0277-786X

International Standard Book Number 13 (ISBN-13)

  • 9781510617759

Digital Object Identifier (DOI)

  • 10.1117/12.2304683

Citation Source

  • Scopus