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Research Spending & Results

Award Detail

Awardee:VECTECH, LLC
Doing Business As Name:VECTECH, LLC
PD/PI:
  • Adam Goodwin
  • (858) 442-4658
  • adam@vectech.io
Award Date:06/11/2021
Estimated Total Award Amount: $ 255,781
Funds Obligated to Date: $ 255,781
  • FY 2021=$255,781
Start Date:06/15/2021
End Date:02/28/2022
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:SBIR Phase I: Advanced‌ ‌Computer‌ Vision‌ ‌Methods‌ ‌for‌ ‌Mosquito‌ ‌Surveillance
Federal Award ID Number:2039534
DUNS ID:117161343
Program:SBIR Phase I
Program Officer:
  • Peter Atherton
  • (703) 292-8772
  • patherto@nsf.gov

Awardee Location

Street:505 PARK AVE APT 2A
City:BALTIMORE
State:MD
ZIP:21201-4789
County:Baltimore
Country:US
Awardee Cong. District:07

Primary Place of Performance

Organization Name:VecTech LLC
Street:1812 Ashland Ave
City:Baltimore
State:MD
ZIP:21205-1546
County:Baltimore
Country:US
Cong. District:07

Abstract at Time of Award

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from the development of new tools to prevent future mosquito disease outbreaks. Most US mosquito control organizations lack the capability or capacity to conduct routine mosquito surveillance, a necessary task for effective mosquito control. Globally, 80% of the world population is at risk for mosquito-borne disease, and mosquito surveillance, monitoring, and evaluation are widely recognized as critical public health activities o be scaled globally. The technology developed through this proposal will develop and demonstrate the feasibility of new identification methods to reduce operational mosquito surveillance costs, while improving accuracy and standardization of data. The result will be improved decisions that will reduce the incidence of mosquito-borne diseases. This Small Business Innovation Research (SBIR) Phase I project will build on advances in computer vision for high accuracy identification of mosquito species in operational contexts. While high accuracy classification of mosquito species has been demonstrated using deep convolutional neural networks (CNNs), evidence has been limited to controlled laboratory environments, with small datasets of few species, or with lab reared specimens. Operational environments face a significantly more complex problem, with hundreds of potential species that may be encountered, and variation in morphology and quality of wild-caught mosquitoes. This proposal seeks to overcome and mitigate the core technical challenges unaddressed by the current state of research, including: fine-grain classification techniques required to distinguish medically relevant species from over three thousand mosquito species found in nature with significant overlapping morphology, novel species detection methods to identify when a presented specimen is from species unknown to the species classification algorithms, and characterizing the feeding state and the physical quality of specimens, such as damage to wings, legs, scales, and body. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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