Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: A deep learning approach

Kevin J. Liang, Geert Heilmann, Christopher Gregory, Souleymane O. Diallo, David Carlson, Gregory P. Spell, John B. Sigman, Kris Roe, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The Transportation Security Administration safeguards all United States air travel. To do so, they employ human inspectors to screen x-ray images of carry-on baggage for threats and other prohibited items, which can be challenging. On the other hand, recent research applying deep learning techniques to computer-aided security screening to assist operators has yielded encouraging results. Deep learning is a subfield of machine learning based on learning abstractions from data, as opposed to engineering features by hand. These techniques have proven to be quite effective in many domains, including computer vision, natural language processing, speech recognition, self-driving cars, and geographical mapping technology. In this paper, we present initial results of a collaboration between Smiths Detection and Duke University funded by the Transportation Security Administration. Using convolutional object detection algorithms trained on annotated x-ray images, we show real-time detection of prohibited items in carry-on luggage. Results of the work so far indicate that this approach can detect selected prohibited items with high accuracy and minimal impact on operational false alarm rates.
Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIEspie@spie.org
ISBN (Print)9781510617759
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

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