Newswise — WASHINGTON—The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) has awarded funding to four research and development (R&D) projects that will improve the threat detection capabilities of current X-ray technologies for passenger X-ray baggage systems.
These projects will be managed by the DHS S&T Checked Baggage Program, part of the Homeland Security Advanced Research Projects Agency Explosives Division (EXD). The Checked Baggage Program supports Transportation Security Administration (TSA) technology R&D requirements to improve overall detection and false alarm performance for explosives detection system (EDS) technologies.
“The emergence of homemade explosives has placed many challenges on aviation security screening,” said DHS Under Secretary (Acting) for Science and Technology, William N. Bryan. “S&T is making important investments in technology that will be leveraged into the next generation of checked baggage screening equipment.”
“We are addressing current, ongoing, and upcoming capability gaps with a three-pronged approach utilizing the continuous transition of hardware, software, and knowledge,” said EXD’s Checked Baggage Program Manager, Sharene Young.
The project contracts were awarded under Broad Agency Announcement HSHQDC-17-R-B0003, which was issued in December 2016. The Broad Agency Announcement consisted of three task areas, focusing on maturation of X-ray technologies for passenger X-ray baggage systems, continued exploration and development of advanced reconstruction algorithm technologies for checked and carry-on baggage, and a focusing of efforts to mature non-Commercial Off the Shelf “long-lead” device technology. The following groups and their projects are the funded BAA awards:
Integrated Defense and Security Solutions (IDSS), of Armonk, New York, was awarded $1,514,868 to improve throughput and detection performance by retrofitting currently-fielded detection systems. The project will create a retrofit kit that will allow TSA to update current, computed tomography (CT) based, in-service EDS for checked baggage with common hardware and software. By developing a common framework in which multiple existing systems use the same hardware and software implementations, IDSS hopes to enhance support for future upgrades to currently deployed systems. This commonality also reduces the number of discrete components in the systems, improving reliability and ease of maintenance, which in turn reduces lifetime costs of the systems. On a technical scale, the retrofit kits have potential to significantly improve the detection and false alarm performance of the existing system, and, consequently, provide an improved rate of passenger throughput.
Integrated Defense and Security Solutions (IDSS), of Armonk, New York, was also awarded $1,915,871 to use machine learning to develop an algorithm to focus on hidden threats. IDSS proposes to use machine learning to develop an algorithm to focus on threats which may be hidden. The low-density nature of certain threats – which can overlap with many benign items in a bag – create a challenge for operators to discriminate between the two. The plan to develop an automated capability to detect prohibited items and laptop threats, with the goal of fully automated clearing of carry-on bags without operator intervention, allows security resources to protect against a wider variety of threats.
Lawrence Livermore National Laboratories (LLNL) of Livermore, California, was awarded $1,825,000 to improve CT data processing techniques for baggage scanning. This project will work to develop algorithms that will improve automated threat recognition software in X-ray detection systems. The LLNL solution will use data and images collected from X-ray imaging systems to help train several proposed threat recognition algorithms. These algorithms will improve on current technology by compensating for beam hardening, a natural effect of the lower energy X-rays being absorbed in materials, which causes dark streaks in X-ray images and can interfere with the operator’s reviewing of the image. The resulting algorithms will be incorporated into a software upgrade for X-ray systems, helping to reduce false alarms while maintaining the rate of passengers and bags moved through an entire checkpoint system.
TeleSecurity Sciences of Las Vegas, Nevada, was awarded $1,511,921 to continue improvements of a vendor-neutral common automated threat recognition (ATR) for EDS. TeleSecurity will develop a method of reducing false alarms by using advanced spectral clustering and gauss Markov random field generator models. These models will help improve the ATR software on fielded systems and increase the probability of detection of prohibited articles. These basic improvements have the potential to reduce the costs of operation and improve security and baggage throughput.
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