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Título: | Advances in electronic monitoring of fishing catches based on artificial intelligence |
Autor: | Ovalle, Juan Carlos CSIC; Velasco, Eva; Vilas Fernández, Carlos CSIC ORCID; Abad, Esther; Valeiras, J.; Pérez Martín, Ricardo Isaac CSIC ORCID; Antelo, L. T. CSIC ORCID | Palabras clave: | Remote Electronic monitoring systems (REMs) Catch identification Species quantification Deep learning Convolutional neural networks |
Fecha de publicación: | 2021 | Editor: | Universidad Politécnica de Cataluña | Citación: | Instrumentation Viewpoint 21: 37-38 (2021) | Resumen: | Monitoring plays a key role in all aspects of fsheries management, including those related to sustainable management of resources, the economic performance of the fshery, and the distribution of benefts from the exploitation of the fshery and environment. In this work, software improvements made on the remote electronic monitoring (REM) device iObserver are described towards the improvement of fsheries monitoring by precisely identifying and quantifying fshing catches on board commercial vessel´s. To this aim, we exploit deep learning and convolutional neural networks (CNNs) capabilities and potential | Descripción: | 9th International Workshop on Marine Technology (MARTECH), virtual, 16-18 June 2021 | Versión del editor: | https://upcommons.upc.edu/handle/2117/360219 | URI: | http://hdl.handle.net/10261/259839 | E-ISSN: | 1886-4864 |
Aparece en las colecciones: | (IIM) Artículos |
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Advances_electronic_2021.pdf | 520,35 kB | Adobe PDF | Visualizar/Abrir |
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