The Maximized Retention Model
The Maximized Retention EM Model is designed to improve accountability and decrease monitoring costs for vessels in the New England Multispecies Groundfish Fishery. In this model, vessels are exempt from minimum size requirements for allocated groundfish species and instead must retain fish that they would otherwise be required to discard (dead) at-sea. EM is used as a compliance tool to ensure all groundfish are landed and, rather than recording discards at-sea, all allocated fish are accounted for on shore by a dockside monitor. GMRI is pioneering novel technology solutions to reduce the cost, logistic and catch handling burdens of EM, improve data transmission time, and build EM system and data utility for participants in the Maximized Retention project.
By documenting catch on shore and streamlining video analysis, the project team is testing the potential for this model to:
- Decrease catch handling burdens at sea
- Reduce the time and cost of video review
- Improve landings data critical to management and stock assessment
- Open new markets for fish that would otherwise become waste at sea
- Increase the accuracy and decrease the uncertainty of fisheries dependent data
GMRI, working in conjunction with the Sustainable Harvest Sector, is in the first year of a 2-year pilot Maximized Retention project. Learn more about the first year of the project in our Year-One Report.
Automation in Electronic Monitoring
Automation of visual imagery analysis has progressed at an amazing rate in the last decade. Many common tasks can now be fully automated, and automation is even used to enhance human performance of difficult tasks. As a result, many fisheries stakeholders in New England are actively pursuing machine learning as a solution to prohibitive human review, data transmission, and storage costs of video analysis.
In an effort to advance the development automation in EM programs the Gulf of Maine Research Institute (GMRI), with its partners CVision AI and New England Marine Monitoring, hosted an electronic monitoring (EM) workshop entitled “Incorporating Machine Learning into Northeast EM programs” on January 23, 2020.
The goal of the workshop was to convene EM project stakeholders, AI specialists, NOAA staff, and data specialists to focus on the potential uses of machine learning in current and future programs, identify regional priorities, and begin to discuss the pathway for incorporating AI into video analysis.
Learn more by exploring the workshop report (Integrated Machine Learning and Electronic Monitoring) or by watching a selection of presentations from the workshop.
To provide further support to groups progressing machine learning and automatic analysis, GMRI partnered with CVision AI to publish Electronic Monitoring: Best Practices for Automation, a first-of-its-kind Electronic Monitoring guidance document that describes best practices for designing EM systems and programs suited for automated or semi-automated video analysis. This document presents a broad range of information for use as a guide to industry groups, governments, or EM vendors considering or currently developing automated EM programs, and provides technical detail for groups that have experience in automated EM programs and an interest in the lessons learned from New England.
|Questions about Electronic Monitoring? Contact Heather Cronin, Electronic Monitoring Project Manager: firstname.lastname@example.org or (207) 228-1687|