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.
Kevin J. Liang, Geert Heilmann, Christopher Gregory, Souleymane O. Diallo, David Carlson, Gregory P. Spell, John B. Sigman, Kris Roe, and Lawrence Carin, "Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: a deep learning approach," Proc. SPIE 10632, Anomaly Detection and Imaging with X-Rays (ADIX) III, 1063203 (Presented at SPIE Defense + Security: April 17, 2018; Published: 27 April 2018); https://doi.org/10.1117/12.2309484.
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