Observer’s Associate

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 242065 Anchorage, AK 99524 907.563.3474 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 observerswith 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 Fish Outline Fish Metrics Image Capture Boundary Detection Measure Fish Identify Species Fish Image Fish Metrics Fish Species Fish Image Data Storage 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 PanelComposition • 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 Scientific Fishery Systems, Inc