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Maria Carmen Hernandez

Bio: Maria Carmen Hernandez is an academic researcher from University of Trás-os-Montes and Alto Douro. The author has contributed to research in topics: Tuna & Sonar. The author has an hindex of 1, co-authored 1 publications receiving 15 citations. Previous affiliations of Maria Carmen Hernandez include University of the Basque Country.
Topics: Tuna, Sonar

Papers
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Journal ArticleDOI
02 Feb 2017-PLOS ONE
TL;DR: 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, and has the potential to automatically analyze high volumes of data at a low cost.
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.

20 citations


Cited by
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Journal ArticleDOI
Yannick Baidai1, Laurent Dagorn1, M.J. Amande, Daniel Gaertner1, Manuela Capello1 
TL;DR: In this article, a novel methodology is presented which utilizes random forest classification to translate the acoustic backscatter from the buoys into metrics of tuna presence and abundance, and the analysis showed accuracies of 75 and 85 % for the recognition of the presence/absence of tuna aggregations under DFADs in the Atlantic and Indian Oceans, respectively.

21 citations

Journal ArticleDOI
TL;DR: In this paper , the authors reviewed the relevant articles on fish stress monitoring and summarized that the novel technologies were sorted into three categories: machine vision-based, sensor-based and acoustic-based methods.

14 citations