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Automatic fish counting from underwater video images: performance estimation and evaluation

AuthorsMarini, S.; Azzurro, Ernesto ; Coco, Salvatore; Río, Joaquín del; Enguídanos, Sergio; Fanelli, Emanuela ; Nogueras, Marc; Sbragaglia, Valerio ; Toma, D.; Aguzzi, Jacopo
KeywordsCabled observatories
Manual fish counts
Automated fish counts
Pattern recognition
Issue DateOct-2016
PublisherUniversidad Politécnica de Cataluña
CitationInstrumentation Viewpoint 19: 55-57 (2016)
AbstractCabled observatories offer new opportunities to monitor species abundances at frequencies and durations never attained before. When nodes bear cameras, these may be transformed into the first sensor capable of quantifying biological activities at individual, populational, species, and community levels, if automation image processing can be sufficiently implemented. Here, we developed a binary classifier for the fish automated recognition based on Genetic Programming tested on the images provided by OBSEA EMSO testing site platform located at 20 m of depth off Vilanova i la Gertrú (Spain). The performance evaluation of the automatic classifier resulted in a 78% of accuracy compared with the manual counting. Considering the huge dimension of data provided by cabled observatories and the difficulty of manual processing, we consider this result highly promising also in view of future implementation of the methodology to increase the accuracy
Description7th International Workshop on Marine Technology – Martech Workshop 2016, 26-28 October 2016, Barcelona.-- 3 pages, 4 figures, 1 table
Publisher version (URL)http://upcommons.upc.edu/handle/2117/99939
Identifiersissn: 1697-2562
e-issn: 1886-4864
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