2420601
Project Grant
Overview
Grant Description
SBIR Phase I: Integrating deep learning algorithms for UAS-based infrastructure inspection: Path to fully automated, commercially viable and scalable monitoring.
The broader/commercial impact of this Small Business Innovation Research Phase I project will be improving the lives of US residents by increasing electric power grid resilience through increased effectiveness and efficiency with automated electric infrastructure monitoring based on imaging with Uncrewed Autonomous System (UAS) (i.e., drones).
Automated UAS monitoring approaches incorporating novel AI algorithms will disrupt conventional approaches, increasing the spatial extent and temporal frequency of infrastructure inspections, and will accelerate identification of all types of defects and reduce operating expenses.
Such tools and technology will also support programs for integration of large-scale renewable-based power projects and electric vehicles to help meet sustainability targets.
They will also reduce wildfire risks and duration of weather-related power shutoffs.
While electric utility infrastructure is the primary focus, inspection and monitoring of myriad infrastructure types such as telecommunication towers, pipelines, and bridges, both in construction and operational phases, will benefit from this technology.
Step-change productivity gains through adoption of digital workflow automation will require workforce role evolution and drive new job creation.
A diverse and skilled company team will be built by emulating the culture of diversity and inclusion of the co-founders' university roots.
This project will facilitate a major leap towards exploiting highly detailed imagery captured by Uncrewed Autonomous System (UAS) to achieve greater performance and automation for infrastructure inspection.
The goal is to integrate time-sequential UAS imagery captured from the same location in the sky, with multiple AI algorithms to achieve both detection and identification of damage to overhead electric infrastructure (and ultimately many types of infrastructure).
The centerpiece of the integrated AI model framework is a model that exploits temporal changes in conditions of electric utility apparatus to detect defects requiring maintenance.
Another AI algorithm will simulate apparatus damages in images used to train AI routines, since actual damage is a relatively rare occurrence within the thousands of inspection images captured by UAS.
A riskier but transformative research element will involve integrating the novel damage detection model with AI models that identify specific damage types from single-time images.
This hybrid modeling approach will restrict the image domain for which damage is identified, to focus the attention of infrastructure inspectors on changes confirmed to be associated with damage.
Temporal image sequences will ultimately feed predictive analytic models that forecast the likelihood of damage or failure and prioritize the timing of inspections.
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.
Subawards are not planned for this award.
The broader/commercial impact of this Small Business Innovation Research Phase I project will be improving the lives of US residents by increasing electric power grid resilience through increased effectiveness and efficiency with automated electric infrastructure monitoring based on imaging with Uncrewed Autonomous System (UAS) (i.e., drones).
Automated UAS monitoring approaches incorporating novel AI algorithms will disrupt conventional approaches, increasing the spatial extent and temporal frequency of infrastructure inspections, and will accelerate identification of all types of defects and reduce operating expenses.
Such tools and technology will also support programs for integration of large-scale renewable-based power projects and electric vehicles to help meet sustainability targets.
They will also reduce wildfire risks and duration of weather-related power shutoffs.
While electric utility infrastructure is the primary focus, inspection and monitoring of myriad infrastructure types such as telecommunication towers, pipelines, and bridges, both in construction and operational phases, will benefit from this technology.
Step-change productivity gains through adoption of digital workflow automation will require workforce role evolution and drive new job creation.
A diverse and skilled company team will be built by emulating the culture of diversity and inclusion of the co-founders' university roots.
This project will facilitate a major leap towards exploiting highly detailed imagery captured by Uncrewed Autonomous System (UAS) to achieve greater performance and automation for infrastructure inspection.
The goal is to integrate time-sequential UAS imagery captured from the same location in the sky, with multiple AI algorithms to achieve both detection and identification of damage to overhead electric infrastructure (and ultimately many types of infrastructure).
The centerpiece of the integrated AI model framework is a model that exploits temporal changes in conditions of electric utility apparatus to detect defects requiring maintenance.
Another AI algorithm will simulate apparatus damages in images used to train AI routines, since actual damage is a relatively rare occurrence within the thousands of inspection images captured by UAS.
A riskier but transformative research element will involve integrating the novel damage detection model with AI models that identify specific damage types from single-time images.
This hybrid modeling approach will restrict the image domain for which damage is identified, to focus the attention of infrastructure inspectors on changes confirmed to be associated with damage.
Temporal image sequences will ultimately feed predictive analytic models that forecast the likelihood of damage or failure and prioritize the timing of inspections.
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.
Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding Agency
Place of Performance
San Diego,
California
92110-1552
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Termination This project grant was reported as terminated by the Department of Government Efficiency (DOGE) in July 2025. See All
Amendment Since initial award the End Date has been extended from 01/31/25 to 04/25/25.
Amendment Since initial award the End Date has been extended from 01/31/25 to 04/25/25.
Changeaerial was awarded
Project Grant 2420601
worth $274,727
from in July 2024 with work to be completed primarily in San Diego California United States.
The grant
has a duration of 9 months and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: Integrating deep learning algorithms for UAS-based infrastructure inspection: Path to fully automated, commercially viable and scalable monitoring
Abstract
The broader/commercial impact of this Small Business Innovation Research Phase I project will be improving the lives of US residents by increasing electric power grid resilience through increased effectiveness and efficiency with automated electric infrastructure monitoring based on imaging with uncrewed autonomous system (UAS) (i.e., drones). Automated UAS monitoring approaches incorporating novel AI algorithms will disrupt conventional approaches, increasing the spatial extent and temporal frequency of infrastructure inspections, and will accelerate identification of all types of defects and reduce operating expenses. Such tools and technology will also support programs for integration of large-scale renewable-based power projects and electric vehicles to help meet sustainability targets. They will also reduce wildfire risks and duration of weather-related power shutoffs. While electric utility infrastructure is the primary focus, inspection and monitoring of myriad infrastructure types such as telecommunication towers, pipelines, and bridges, both in construction and operational phases, will benefit from this technology. Step-change productivity gains through adoption of digital workflow automation will require workforce role evolution and drive new job creation. A diverse and skilled company team will be built by emulating the culture of diversity and inclusion of the co-founders’ university roots.
This project will facilitate a major leap towards exploiting highly detailed imagery captured by uncrewed autonomous system (UAS) to achieve greater performance and automation for infrastructure inspection. The goal is to integrate time-sequential UAS imagery captured from the same location in the sky, with multiple AI algorithms to achieve both detection and identification of damage to overhead electric infrastructure (and ultimately many types of infrastructure). The centerpiece of the integrated AI model framework is a model that exploits temporal changes in conditions of electric utility apparatus to detect defects requiring maintenance. Another AI algorithm will simulate apparatus damages in images used to train AI routines, since actual damage is a relatively rare occurrence within the thousands of inspection images captured by UAS. A riskier but transformative research element will involve integrating the novel damage detection model with AI models that identify specific damage types from single-time images. This hybrid modeling approach will restrict the image domain for which damage is identified, to focus the attention of infrastructure inspectors on changes confirmed to be associated with damage. Temporal image sequences will ultimately feed predictive analytic models that forecast the likelihood of damage or failure and prioritize the timing of inspections.
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.
Topic Code
R
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 5/19/25
Period of Performance
7/15/24
Start Date
4/25/25
End Date
Funding Split
$274.7K
Federal Obligation
$0.0
Non-Federal Obligation
$274.7K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2420601
Additional Detail
Award ID FAIN
2420601
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
K1EZDVCZHSN7
Awardee CAGE
None
Performance District
CA-51
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Modified: 5/19/25