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Detecting the presence-absence of bluefin tuna by automated analysis of medium-range sonars on fishing vessels

Author

Listed:
  • Jon Uranga
  • Haritz Arrizabalaga
  • Guillermo Boyra
  • Maria Carmen Hernandez
  • Nicolas Goñi
  • Igor Arregui
  • Jose A Fernandes
  • Yosu Yurramendi
  • Josu Santiago

Abstract

This study presents a methodology for the automated analysis of commercial medium-range sonar signals for detecting presence/absence of bluefin tuna (Tunnus thynnus) in the Bay of Biscay. The approach uses image processing techniques to analyze sonar screenshots. For each sonar image we extracted measurable regions and analyzed their characteristics. Scientific data was used to classify each region into a class (“tuna” or “no-tuna”) and build a dataset to train and evaluate classification models by using supervised learning. The methodology performed well when validated with commercial sonar screenshots, and has the potential to automatically analyze high volumes of data at a low cost. This represents a first milestone towards the development of acoustic, fishery-independent indices of abundance for bluefin tuna in the Bay of Biscay. Future research lines and additional alternatives to inform stock assessments are also discussed.

Suggested Citation

  • Jon Uranga & Haritz Arrizabalaga & Guillermo Boyra & Maria Carmen Hernandez & Nicolas Goñi & Igor Arregui & Jose A Fernandes & Yosu Yurramendi & Josu Santiago, 2017. "Detecting the presence-absence of bluefin tuna by automated analysis of medium-range sonars on fishing vessels," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-18, February.
    • Handle: RePEc:plo:pone00:0171382
    DOI: 10.1371/journal.pone.0171382
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    References listed on IDEAS

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    1. Fromentin, Jean-Marc & Bonhommeau, Sylvain & Arrizabalaga, Haritz & Kell, Laurence T., 2014. "The spectre of uncertainty in management of exploited fish stocks: The illustrative case of Atlantic bluefin tuna," Marine Policy, Elsevier, vol. 47(C), pages 8-14.
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