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The emergence of homemade explosives has placed many challenges on aviation security screening. In an attempt to cope with the threats, the US Department of Homeland Security (DHS) Science and Technology Directorate (S&T) has been making substantial investments in technology that could be leveraged into the next generation of checked baggage screening equipment.

The DHS S&T has awarded a total of nearly $3.5 million in funding to three new research and development projects designed to improve the threat detection capabilities of current X-ray technologies for checked baggage systems. “We are addressing current, ongoing, and upcoming capability gaps with a three-pronged approach utilizing the continuous transition of hardware, software, and knowledge,” said S&T Checked Baggage Program Manager, Sharene Young.

The program is consisted of improving X-ray technologies for bag screening systems, developing advanced algorithm technologies for checked and carry-on baggage, and focusing efforts to refine non-commercial off-the-Shelf long-term device technology, according to the announcement on dhs.gov.

The projects will be managed by the DHS S&T Checked Baggage Program, which supports TSA requirements to improve overall detection and false alarm performance for explosives detection system technologies.


The first project is an automated threat detection algorithm for improved detection of prohibited items such as guns and knives. A deep learning 3D convolutional neural network approach will be used to enhance algorithm development. The goal is to deploy the automated threat recognition (ATR) algorithm on the TSA’s checkpoint computed tomography (CT) systems. The company behind this project is Capture.

Another project will produce 12 large field-of-view, high-output count rate X-ray imaging arrays with high spatial and energy resolution which can operate at room temperature and be manufactured cost-effectively.

The technology will help eliminate false positives in primary screening lanes by adding inline X-ray diffraction (XRD). The new technology directly addresses issues by delivering a better detector with better resolution that can be added in series to existing primary lanes. The project was presented by DxRay/Rapiscan.

The third project is designed to improve high-speed coded-aperture X-ray scatter imaging (CAXSI) screening to stream-of-commerce rates. The project focuses on high-speed data acquisition and maximizing the count rate through the detector module without compromising other capabilities. This will allow X-ray machines to be more efficient, with both better detection and lower energy needs. The project will be developed by EV Products.