Advances in electronic monitoring of fishing catches based on artificial intelligence
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...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/360219 |
| Acceso en línea: | https://hdl.handle.net/2117/360219 |
| Access Level: | acceso abierto |
| Palabra clave: | Remote control Fishing Fish populations Remote electronic monitoring systems (REMs) Catch identifcation Species quantifcation Deep learning Convolutional neural networks Telecontrol Pesca Peixos -- Poblacions Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | 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. |
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