DHS Awards $1 Million to Support Machine Learning Development for Airport Security

With a growing need to improve the security, efficiency and accuracy of passenger and baggage screening, the Department of Homeland Security (DHS) Small Business Innovation Research (SBIR) Program is working with a small business to advance explosive detection equipment. Synthetik Applied Technologies was awarded funding to develop machine learning training data that simulates human travelers and baggage object models to support machine learning algorithms.

“As threats to our nation’s airports continue to evolve, we are committed to investing in technologies that will improve the security posture of aviation checkpoints, while minimizing the inconvenience to passengers,” said William N. Bryan, DHS Senior Official Performing the Duties of the Under Secretary for Science and Technology. “We look forward to seeing the technology developed through the SBIR Program that supports our vision for a passenger screening process that is reliable, less invasive, and efficient.”

The DHS SBIR Program, administered by DHS Science and Technology Directorate (S&T), selected Synthetik Applied Technologies, based in Austin, Texas, to participate in Phase II of the program, based on the successful demonstration of feasibility in Phase I for their Synethic Data Training For Explosive Detection Machine Learning Algorithms technology solution.

In Phase II, Synthetik Applied Technologies will continue their efforts to develop synthetic training data that will enhance machine learning object detection algorithms to improve detection and reduce false alarms. For machine learning algorithms to reach their peak performance, they must be trained on a very large amount of data, and collecting and preparing this data is typically an expensive and time-consuming process. Synthetic data generation creates the capability to generate complete, annotated datasets in a matter of minutes without handling dangerous materials or initiating human subjects’ protocols. This technology would streamline the security screening process, creating an improved passenger experience for the traveling public.

“Synthetik’s work will enable DHS S&T’s Screening at Speed Program to generate high-fidelity training data for machine learning algorithms virtually instantaneously and with very little cost,” said Karl Harris, DHS S&T Program Manager. “This training data will help us develop faster and more accurate algorithms to improve throughput of passenger bags while protecting the health and safety of Transportation Security Administration (TSA) employees and the traveling public.”

This effort started prior to the COVID-19 pandemic, but has become even more relevant as social distancing and other protective measures are put into place in order to minimize the exposure and contact between TSA officers and passengers.

At the completion of the 24-month Phase II contract, SBIR awardees will have received up to $1 million to develop and demonstrate a prototype to facilitate the pursuit of Phase III funding. For Phase III, SBIR performers seek to secure funding from private or a non-SBIR government source and pursue technology commercialization resulting from their Phase I and II efforts.

For more information on the DHS SBIR Program, visit: https://sbir2.st.dhs.gov or contact STSBIR.Program@hq.dhs.gov.

For more information on S&T’s innovation programs and tools, visit: https://www.dhs.gov/science-and-technology/business-opportunities.

Read more at DHS S&T

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The Government Technology & Services Coalition's Homeland Security Today (HSToday) is the premier news and information resource for the homeland security community, dedicated to elevating the discussions and insights that can support a safe and secure nation. A non-profit magazine and media platform, HSToday provides readers with the whole story, placing facts and comments in context to inform debate and drive realistic solutions to some of the nation’s most vexing security challenges.

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