WASHINGTON – The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) awarded $142,465 in Phase 1 funding to Cignal LLC of Ashburn, Virginia, to create high-fidelity synthetic data used to train artificial intelligence (AI). S&T’s Silicon Valley Innovation Program’s (SVIP) is funding this work under its “Object Recognition and Adaptive Algorithms in Passenger Property Screening” solicitation.
Cignal creates new software systems and capabilities using high-fidelity synthetic data that facilitate the rapid training and deployment of advanced vision systems.
“Having a source for computer-generated and annotated training images will significantly reduce the time and expense for fielding new machine learning models for more efficient screening and in response to new threats” said Karl Harris, S&T Screening at Speed Program Manager. “Generating large amounts of training data is typically a very labor-intensive, manual process, with some object recognition models requiring tens of thousands of labeled images per object class.”
Cignal project proposes to enhance its working prototype and training workflow product, Cignal Workbench, to generate high-fidelity synthetic training data for Advanced Technology (AT) X-ray inspection systems and synthetic volumetric data for Computed Tomography (CT) applications. The result will be a high-volume data source for labeled baggage for seamless, unsupervised, and continuous AI model training.
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