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High Speed High Accuracy Artificial Neural Networks for UAV based Target Identification

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-19-C-0047
Agency Tracking Number: F18B-007-0077
Amount: $149,998.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF18B-T007
Solicitation Number: 18.B
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-01-30
Award End Date (Contract End Date): 2019-01-30
Small Business Information
1708 Jaggie Fox Way
Lexington, KY 40511
United States
DUNS: 014005016
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Manuel G Garcia Jr
 VP for Product Development
 (210) 863-3810
Business Contact
 Nalin Kumar
Phone: (817) 880-3880
Research Institution
 University of Texas at Arlington
 Sarah Panepinto Sarah Panepinto
202 E. Border Street STE 216
Arlington, TX 76010
United States

 (817) 272-2105
 Nonprofit College or University

The machine learning and artificial intelligence community has recently garnered much attention for ground breaking performance of novel neural network architectures for self-driving cars. One of the machine learning methods used in self-driving cars is semantic segmentation. In this fashion each pixel in an image is label with a class, allowing for contour-based image segmentation which is different than the traditional sliding windows technique used in convolutional neural networks. UHV has previously developed a contour-based image segmentation technique for several of its commercially available products which include machinery in industrial recycling. In this Phase I effort UHV Technologies will develop innovative methods for target identification for UAV and UAS with state of the art machine learning methods in semantic segmentation, by modifying their existing ML/AI software, then demonstrate the technology at one of the nation’s six major test sites for UAVs and UASs with UTARI. The Phase II work will include optimizing SWaP parameters in addition to customizing the algorithms for specific applications.

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

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