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

MacKenzie, BR; Uranga, J; Arrizabalaga, H; Boyra, G; Hernandez, MC; Goñi, N; Arregui, I; Fernandes, JA; Yurramendi, Y; Santiago, J. 2017 Detecting the presence-absence of bluefin tuna by automated analysis of medium-range sonars on fishing vessels. PLOS ONE, 12 (2). e0171382. https://doi.org/10.1371/journal.pone.0171382

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Official URL: https://doi.org/10.1371/journal.pone.0171382

Abstract/Summary

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.

Item Type: Publication - Article
Divisions: Plymouth Marine Laboratory > Science Areas > Earth Observation Science and Applications
Depositing User: Kim Hockley
Date made live: 20 Feb 2019 16:31
Last Modified: 25 Apr 2020 09:59
URI: http://plymsea.ac.uk/id/eprint/8139

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