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Lessons learned: data mining and aviation explosives detection systems
Author(s): Matthew Merzbacher
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Paper Abstract

Prior to the advent of modern deep learning techniques, data mining was already being used for image processing in aviation security. In 2010, the paper “Applying data mining to false alarm reduction in an aviation explosives detection system”, detailed lessons learned from using automated data mining techniques for false alarm identification. The paper included a series of observations and recommendations. Nearly a decade later, deep learning is showing tremendous promise for a variety of image processing problems (in general) and to CT-based explosives detection systems (EDS) in particular. While some risks and shortcomings of deep learning are understood, the particular issues associated with aviation security applications may not be. We revisit the earlier work and see whether it withstands the test of time and still applies. We then combine the earlier work with modern deep learning design guidelines, to form a guide to using deep learning for aviation security.

Paper Details

Date Published: 14 May 2019
PDF: 6 pages
Proc. SPIE 10999, Anomaly Detection and Imaging with X-Rays (ADIX) IV, 109990M (14 May 2019); doi: 10.1117/12.2518776
Show Author Affiliations
Matthew Merzbacher, Smiths Detection Inc. (United States)


Published in SPIE Proceedings Vol. 10999:
Anomaly Detection and Imaging with X-Rays (ADIX) IV
Amit Ashok; Joel A. Greenberg; Michael E. Gehm, Editor(s)

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