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Novel Mathematical Foundation for Automated Annotation of Massive Image Data Sets

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047622C0057
Agency Tracking Number: NGA-P1-22-03
Amount: $99,995.65
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NGA203-005
Solicitation Number: 20.3
Solicitation Year: 2020
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-01-27
Award End Date (Contract End Date): 2022-11-14
Small Business Information
5717 Huberville Avenue Suite 300
Dayton, OH 45431-1111
United States
DUNS: 002231525
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Stephen Rosencrantz
 (937) 252-2710
Business Contact
 Daniel C. Cyphers
Phone: (937) 252-2710
Research Institution

Modern Artificial Intelligence (AI) solutions generally employ carefully-crafted Neural Networks (NNs) that require extensive human effort to perform detection, identification, and annotation on each image to create training datasets. AI tools are desired that are optimized for object identification and annotation across diverse families of image data, are reliable and robust, not dependent on extensive training demands, and are applicable to objects of interest for both government and commercial concerns. AI outputs that are explainable and more “lightweight” to human users are needed to overcome these limitations. To remove the demand for human annotation for object detection, Skyward, Ltd. (Skyward), proposes a methodology to produce an Automated Annotation Toolkit (AAT) that takes advantage of object features to reduce labeling demands and sensitivity to view. The AAT incorporates explainability and interpretability methods to provide intuitive outputs for end users.

* Information listed above is at the time of submission. *

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