A Deep Learning Approach to Photo–identification Demonstrates High Performance on Two Dozen Cetacean Species

Philip T. Patton,Ted Cheeseman, Kenshin Abe,Taiki Yamaguchi,Walter Reade,Ken Southerland,Addison Howard,Erin M. Oleson,Jason B. Allen,Erin Ashe,Aline Athayde,Robin W. Baird,Charla Basran,Elsa Cabrera,John Calambokidis,Julio Cardoso,Emma L. Carroll,Amina Cesario,Barbara J. Cheney,Enrico Corsi,Jens Currie,John W. Durban,Erin A. Falcone,Holly Fearnbach,Kiirsten Flynn,Trish Franklin,Wally Franklin,Barbara Galletti Vernazzani,Tilen Genov,Marie Hill,David R. Johnston,Erin L. Keene,Sabre D. Mahaffy,Tamara L. McGuire, Liah McPherson, Catherine Meyer,Robert Michaud,Anastasia Miliou,Dara N. Orbach,Heidi C. Pearson,Marianne H. Rasmussen,William J. Rayment,Caroline Rinaldi,Renato Rinaldi,Salvatore Siciliano,Stephanie Stack,Beatriz Tintore,Leigh G. Torres,Jared R. Towers,Cameron Trotter, Reny Tyson Moore,Caroline R. Weir,Rebecca Wellard,Randall Wells,Kymberly M. Yano,Jochen R. Zaeschmar,Lars Bejder

METHODS IN ECOLOGY AND EVOLUTION（2023）

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Key words

artificial intelligence,cetacean,computer vision,convolutional neural network,deep learning,dolphin,dorsal,lateral,machine learning,multi-species,photo-identification,whale

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