The Department of Homeland Security (DHS), as part of its Small Business Innovation Research (SBIR) program, has awarded Austin, Texas-based Synthetik funding to develop machine learning training data that simulates human travelers and baggage object models in airports.

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

In Phase II, Synthetik  will continue their efforts to develop synthetic training data that will enhance machine learning object detection algorithms, the DHS said in a statement.

“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.”

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.

[Image courtesy: Synthetik]