Automatic fish counting from underwater video images: performance estimation and evaluation
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/99939
Tipus de documentComunicació de congrés
Data publicació2016
EditorSARTI
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Cabled 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.
CitacióMarini, S. [et al.]. Automatic fish counting from underwater video images: performance estimation and evaluation. A: 7th International Workshop on Marine Technology : MARTECH 2016. "Instrumentation viewpoint". Vilanova i la Geltrú: SARTI, 2016, p. 55-57.
Dipòsit legalB-32814-2006
ISSN1886-4864
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
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ID23.pdf | Abstract | 2,530Mb | Visualitza/Obre |