The detection of prohibited items at airport checkpoints, especially energetic materials, by means of x-ray imaging technology, is one of the most important tasks in transportation security. Conventional checkpoint X-ray systems exploit the energy dependence of the material- specific attenuation coefficient to estimate an ‘effective’ atomic number (or Z<sub>eff</sub> ) and, in some cases, the mass density (ρ) of a target material, which are then used to classify it. While this technology provides high quality imaging capabilities and satisfactory objects discrimination in many security applications, it also has known limitations. For example, differentiating objects with similar Z<sub>eff</sub> and/or ρ, such as is often the case for many benign organic materials and explosives, can be a challenging task. X-ray Diffraction Tomography (XRDT), using a coded mask (down stream from the sample), provides structural information that can further enhance material discrimination from the unique chemical/molecular signatures. Here, we present experimental data obtained using our research prototype or ‘XRDT’ scanner, built with off-the shelf components. Using two different industrial solvents, one benign (H<sub>2</sub>O or water) and one prohibited chemical precursor (2-butanone or methyl-ethyl-ketone (MEK)), we have evaluated the detection performance against material type, sample size, beam size, and investigated the effects of background. Within the scope of our study, we find that a satisfactory tomographic reconstruction and reliable bulk material identification can be achieved with the XRDT. These results will help guide the future development of coded aperture based screening technology at security checkpoint.
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.
Conference Committee Involvement (2)
Anomaly Detection and Imaging with X-Rays (ADIX) IV
15 April 2019 | Baltimore, Maryland, United States
Anomaly Detection and Imaging with X-Rays (ADIX) III