Fish species identification using a convolutional neural network trained on synthetic data
Author:
Affiliation:
1. Institute of Marine Research, Nordnes, Bergen, Norway
2. Department of Applied Mathematics and Modeling, Polytech Nice-Sophia, Sophia Antipolis Cedex, France
3. Department of Informatics, University of Bergen, Bergen, Norway
Abstract
Funder
Research Council of Norway
Norwegian Ministry of Trade, Industry and Fisheries
Scantrol Deep Vision
Publisher
Oxford University Press (OUP)
Subject
Ecology,Aquatic Science,Ecology, Evolution, Behavior and Systematics,Oceanography
Link
http://academic.oup.com/icesjms/article-pdf/76/1/342/31238015/fsy147.pdf
Reference28 articles.
1. Segnet: a deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE Transactions on Pattern Analysis and Machine Intelligence,2017
2. Linearity of fisheries acoustics, with addition theorems;Foote;The Journal of the Acoustical Society of America,1983
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