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Your search keyword '"Vilas Fernández, Carlos"' showing total 2 results
Start Over "Vilas Fernández, Carlos" Topic deep learning
2 results on '"Vilas Fernández, Carlos"'

1. Advances in electronic monitoring of fishing catches based on artificial intelligence

Author

Ovalle, J. C., Velasco, Eva, Vilas Fernández, Carlos, Abad, Esther, Valeiras, J., Pérez Martín, Ricardo Isaac, and Antelo, L. T.

Subjects
Catch identification, Species quantification, Deep learning, Convolutional neural networks, Remote Electronic monitoring systems (REMs)
Abstract

9th International Workshop on Marine Technology (MARTECH), virtual, 16-18 June 2021, 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

Published
2021

2. Advances in electronic monitoring of fishing catches based on artificial intelligence

Author

Ovalle Macías, Juan Carlos, Velasco Gil, Eva María, Vilas Fernández, Carlos, Abad Casas, Esther, Valeiras Mota, Julio, Pérez Martín, Ricardo, and Taboada Antelo, Luis

Subjects
Remote control, Fish populations, Remote electronic monitoring systems (REMs), Species quantifcation, Pesca, Enginyeria electrònica::Instrumentació i mesura [Àrees temàtiques de la UPC], Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC], Fishing, Deep learning, Convolutional neural networks, Peixos -- Poblacions, Catch identifcation, Telecontrol
Abstract

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.

Published
2021

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