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Camera-based Examination of Risk via Behavioral Evaluation with Remote Underwater Surveillance

Award Information
Agency: Department of Commerce
Branch: National Oceanic and Atmospheric Administration
Contract: WC-133R-18-CN-0058
Agency Tracking Number: 17-2-032
Amount: $399,497.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: 8.4.1
Solicitation Number: NOAA-2017-2
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-05-29
Award End Date (Contract End Date): 2021-05-28
Small Business Information
73-4460 Queen Kaahumanu Hwy #104
Kailua Kona, HI 96740-2637
United States
DUNS: 079495304
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Mathew Goldsborough
 Director of Engineering
 (646) 979-0066
Business Contact
 Mathew Goldsborough
Title: Director of Engineering
Phone: (646) 979-0066
Research Institution

TECHNICAL ABSTRACT: Commercial marine aquaculture operators face many operational hazards including disease, predators, husbandry operations, and environmental changes. Most of theses risks are only identified with constant surveillance and physical presence at a farm site. However, human observation of risk factors is expensive, slow, and sometimes ineffective. Sensors are available to monitor individual environmental parameters, but comprehensive monitoring of all operational risks is currently infeasible or cost-prohibitive. This project seeks to develop a single, inexpensive tool, CERBERUS (Camera-based Examination of Risk via Behavioral Evaluation with Remote Underwater Surveillance), to detect and alert operators to the presence of multiple types of operational hazards through the use of low-cost hardware and intelligent software processing. CERBERUS will enable fish farmers to remotely and automatically monitor their stock for responses to such hazards, helping them reduce reaction time in rectifying the causal issues, improve outcomes, and decrease overall operational risk.SUMMARY OF ANTICIPATED RESULTS: Phase II will build upon the advances made in Phase I and result in the development a cloudbased, computer vision framework which will facilitate the acquisition, segmentation, and classification of continuous, real-time video, rapid training and testing of neural network models, and the management of the entire process from a web-enabled dashboard.

* Information listed above is at the time of submission. *

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