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:2117/103822
Tipus de documentText en actes de congrés
Data publicació2016
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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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., Azzurro, E., Coco, S., Del Río, J., Nogueras, M., Sbragaglia, V., Toma, D.M., Aguzzi, J. Automatic fish counting from underwater video images: performance estimation and evaluation. A: International Workshop on Marine Technology. "MARTECH 2016: 7th International Workshop on Marine Technology, Barcelona October 26th, 27th and 28th, 2016". Lisbon: 2016, p. 67-70.
ISBN978-84-617-4152-6
Versió de l'editorhttp://www.upc.edu/cdsarti/martech/usb_2016/paginas/articulos_id/id.html
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Automatic fish.pdf | 284,3Kb | Visualitza/Obre |