Sbir phase I: computer vision for merchandizing forest products

This National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) Project Grant award in the amount of $275,000 supports the development of a computer vision system to assist in grading and sorting harvested logs for the logging industry. The project aims to create a novel computer vision system that can identify, track, and assess the quality of individual logs, providing feedback to human machine operators within brief decision timeframes. The research involves developing self-supervised learning techniques to build a domain-specific dataset, along with adaptable computer vision models and efficient edge inferencing capabilities to enable deployment at remote logging sites. If successful, this technology could improve the utilization of harvested trees, increase revenues for small wood supply businesses, and create more jobs in rural areas. The award was granted to Hendtech LLC, a for-profit limited liability company, and is scheduled to run from February 2024 to January 2025.

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.- subawards are not planned for this award.