Scientific Fishery Systems, Inc 1 Observer’s Associate A consistent, unbiased system using machine vision and fish morphometrics to identify species From Scientific Fishery Systems, Inc. P.O. Box Anchorage, AK Dr. Eric O. Rogers

Scientific Fishery Systems, Inc 2 Observer’s Associate Team Principle Investigator - Pat Simpson - SciFish Lead Scientist Eric O. Rogers, PhD (Physics) - SciFish Luke Jadamec, Fisheries Observer Trainer Joe Imlach PE, PhD (ME) Imlach Consulting Chris Bublitz, UAF Fisheries Industrial Technology Center

Scientific Fishery Systems, Inc 3 Issues identified by SciFish Increasing pressure on resource Increasing complexity of new legislation Possible environmental changes affecting fishery in unknown ways Appropriately harvesting and managing the fishery are increasingly difficult tasks => Need the best data possible <=

Scientific Fishery Systems, Inc 4 Current Sources of Data AFSC Survey Trawls –Practical limits to time and scope Observer’s Reports –Most effective means of monitoring CPUE –Statistically small sample –Potentially biased by factors outside observer’s and vessel operators control –Of questionable value in legal action due to statistical nature of data

Scientific Fishery Systems, Inc 5 SciFish’s Proposal Using funding form the NSF build and test an automated onboard fish cataloging system using COTS Hardware and Software that will: –Assist commercial fishery observers with their monitoring and assessment tasks at sea –Provide detailed unbiased species counts to manage the Community Development Quota (CDQ) program in Western Alaska –Provide new detailed information on the ecological health of each species to assist in fisheries management –Provide detailed information on fish morphometrics that will be of value to researchers in several academic areas, such as fish population studies and fish evolution

Scientific Fishery Systems, Inc 6 Key Concepts COTS hardware and software Candle the fish to separate from background Machine Vision and Morphometrics Neural Net Sample all the fish System scales - can add CPU’s for faster processing and add metrics and/or color for greater accuracy

Scientific Fishery Systems, Inc 7 Observer’s Associate Benefits More and better data means fewer surprises for managers and skippers A healthier fishery through management based upon more complete knowledge Sample entire catch, no extrapolation Fair and impartial catch statistics - a level playing field Easy to identify and reward “clean” Vs “dirty” boats Brings in non-traditional funds for fisheries research (NSF $) Fringe Benefit => Provides length, width, etc. for each fish in addition to species

Scientific Fishery Systems, Inc 8 Observer’s Associate Mechanical Design

Scientific Fishery Systems, Inc 9 Observer’s Associate Logic Flow Image Capture Boundary Detection Measure Fish Identify Species Data Storage Fish Image Fish Metrics Fish Species Fish Outline Fish Image Fish Metrics

Scientific Fishery Systems, Inc 10 Flatfish Features Used by People

Scientific Fishery Systems, Inc 11 Typical Flatfish Features Used by Machine Vision  Body Width  Standard Length  Tail Length  Standard Length  Tail Fork Length or Max width to tip for rounded tails  Standard Length  Body Width  Standard Length  (Total Width  Body Width) / Standard Length  (Ellipse {standard length and body width} - body perimeter)  Standard Length  “Fin” Perimeter (Total Perimeter – Body Perimeter)  Standard Length  (Ellipse Area – Body Area)  (Standard Length * Body Depth)  Fin Area / (Standard Length) 2

Scientific Fishery Systems, Inc 12 Concept Test Scan Pictures from Northeast Pacific Flatfishes Book –Scale to meter stick in picture –Extract measurements Reduce measurements to independent metrics –Principle component analysis Train Neural Net Create 100 fish / species by adding various percentages of white noise Test classifier with “white noise” fish

Scientific Fishery Systems, Inc 13 Normalized Machine Vision Flatfish Metrics Metrics after reduction to Principle Component Vectors

Scientific Fishery Systems, Inc 14 Neural Net Classification Results

Scientific Fishery Systems, Inc 15 Observer’s Tasks Identify Species that Observer’s Associate does not Quality Control Ensure Appropriate Sampling Operate the Observer’s Associate Ensure data integrity and file reports

Scientific Fishery Systems, Inc 16 Plan Assemble Advisory Panel Apply for ASTF Bridge Grant Build “Proof of Concept” Prototype Train and Test Prototype Apply for NSF Phase II Grant Build true prototype Test for volume onshore Test for suitability at sea Initial implementation in the Yellowfin Sole fishery

Scientific Fishery Systems, Inc 17 Advisors Panel Composition Regulators Conservationists Fisheries Scientists CDQ Groups Fishermen Owners Fisheries Consultants

Scientific Fishery Systems, Inc 18 Advisory Panel Questions Are the issues identified by SciFish of Concern to the industry? Is the technology presented a viable solution? Are the other, more appropriate solutions to the problems? What is the best way to implement this solution? Design Changes? Are there other applications to add value to the system? Number of classes for fish Vs accuracy of classification, Vs throughput of fish Vs cost