UHV Technologies Inc — Department of Defense STTR Phase I: AF18B-T007
UHV Technologies Inc — STTR Phase I award from Department of Defense.
Phase I STTR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Defense in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $149,998. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code AF18B-T007 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $149,998
- Agency
- Department of Defense · Air Force
- Program / Phase
- STTR · Phase I
- Topic
- AF18B-T007
- Solicitation
- 18.B
- NAICS
- —
- Place of performance
- KY
- Period
- 2019-01-30 → 2019-01-30
Description
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.