2329601
Project Grant
Overview
Grant Description
Sbir Phase I: Computer Vision for Merchandizing Forest Products -The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from applying leading-edge artificial intelligence technology to the logging industry. Logging (wood harvesting) is the first step in the production of paper packaging, health and hygiene products, clothing fibers, and even resins that are used in our technology devices.
The forest products industry employs nearly one million people and contributes hundreds of billions of dollars to the US economy each year. Yet, there has been very little technological innovation in logging in recent decades. This project aims to research and develop computer vision technology that will augment a person?s ability to grade harvested trees accurately.
The resulting technology may increase the number of jobs available to unskilled workers in rural areas, ensure effective and efficient utilization of harvested trees, and increase revenues for thousands of small businesses in the wood supply chain. This Small Business Innovation Research (SBIR) Phase I project aims to demonstrate the feasibility of a computer vision system to augment the skills of human-machine operators in the tasks of grading and sorting logs.
During the project, a suitable domain-specific dataset will be established, a new chain of computer vision models will be created and trained, and a fully integrated prototype will be deployed in a remote environment. To achieve these goals, the company will research the use of self-supervised learning to expedite the creation of a domain-specific dataset, along with adaptable chains of models and model compression to enable efficient inference at the logging site (i.e., without the need for cloud computing resources).
The company will also create new methods for determining specific objects of interest (such as defects) and assessing the grade of each log. If successful, the project will demonstrate a computer vision system that is able to identify and locate specific logs of interest, track the logs, assess each log?s dimensions, locate defects on the logs, accurately determine a grade for each log, and give visual feedback to a machine operator ? all within the operator?s brief decision timeframe.
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 forest products industry employs nearly one million people and contributes hundreds of billions of dollars to the US economy each year. Yet, there has been very little technological innovation in logging in recent decades. This project aims to research and develop computer vision technology that will augment a person?s ability to grade harvested trees accurately.
The resulting technology may increase the number of jobs available to unskilled workers in rural areas, ensure effective and efficient utilization of harvested trees, and increase revenues for thousands of small businesses in the wood supply chain. This Small Business Innovation Research (SBIR) Phase I project aims to demonstrate the feasibility of a computer vision system to augment the skills of human-machine operators in the tasks of grading and sorting logs.
During the project, a suitable domain-specific dataset will be established, a new chain of computer vision models will be created and trained, and a fully integrated prototype will be deployed in a remote environment. To achieve these goals, the company will research the use of self-supervised learning to expedite the creation of a domain-specific dataset, along with adaptable chains of models and model compression to enable efficient inference at the logging site (i.e., without the need for cloud computing resources).
The company will also create new methods for determining specific objects of interest (such as defects) and assessing the grade of each log. If successful, the project will demonstrate a computer vision system that is able to identify and locate specific logs of interest, track the logs, assess each log?s dimensions, locate defects on the logs, accurately determine a grade for each log, and give visual feedback to a machine operator ? all within the operator?s brief decision timeframe.
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 / Funding Agency
Place of Performance
Simpsonville,
South Carolina
29681-4294
United States
Geographic Scope
Single Zip Code
Hendtech was awarded
Project Grant 2329601
worth $275,000
from National Science Foundation in February 2024 with work to be completed primarily in Simpsonville South Carolina United States.
The grant
has a duration of 1 year 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: Computer Vision for Merchandizing Forest Products
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from applying leading-edge artificial intelligence technology to the logging industry. Logging (wood harvesting) is the first step in the production of paper packaging, health and hygiene products, clothing fibers, and even resins that are used in our technology devices. The forest products industry employs nearly one million people and contributes hundreds of billions of dollars to the US economy each year. Yet, there has been very little technological innovation in logging in recent decades. This project aims to research and develop computer vision technology that will augment a person’s ability to grade harvested trees accurately. The resulting technology may increase the number of jobs available to unskilled workers in rural areas, ensure effective and efficient utilization of harvested trees, and increase revenues for thousands of small businesses in the wood supply chain.
This Small Business Innovation Research (SBIR) Phase I project aims to demonstrate the feasibility of a computer vision system to augment the skills of human-machine operators in the tasks of grading and sorting logs. During the project, a suitable domain-specific dataset will be established, a new chain of computer vision models will be created and trained, and a fully integrated prototype will be deployed in a remote environment. To achieve these goals, the company will research the use of self-supervised learning to expedite the creation of a domain-specific dataset, along with adaptable chains of models and model compression to enable efficient inference at the logging site (i.e., without the need for cloud computing resources). The company will also create new methods for determining specific objects of interest (such as defects) and assessing the grade of each log. If successful, the project will demonstrate a computer vision system that is able to identify and locate specific logs of interest, track the logs, assess each log’s dimensions, locate defects on the logs, accurately determine a grade for each log, and give visual feedback to a machine operator – all within the operator’s brief decision timeframe.
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
AI
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 2/20/24
Period of Performance
2/15/24
Start Date
1/31/25
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2329601
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
M55UM1G1CBL5
Awardee CAGE
9GYA5
Performance District
SC-04
Senators
Lindsey Graham
Tim Scott
Tim Scott
Modified: 2/20/24