Lessons learned: data mining and aviation explosives detection systems

Matthew Merzbacher

doi:10.1117/12.2518776

14 May 2019 Lessons learned: data mining and aviation explosives detection systems

Matthew Merzbacher

Proceedings Volume 10999, Anomaly Detection and Imaging with X-Rays (ADIX) IV; 109990M (2019) https://doi.org/10.1117/12.2518776
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Conference Presentation

Citation Download Citation

Matthew Merzbacher "Lessons learned: data mining and aviation explosives detection systems", Proc. SPIE 10999, Anomaly Detection and Imaging with X-Rays (ADIX) IV, 109990M (14 May 2019); https://doi.org/10.1117/12.2518776

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