Detect and follow a custom object, using OBSEA underwater crawler

The strategic advancement of monitoring protocols relies on cabled marine observatories for obtaining real-time multiparametric biological and environmental data. The integration of docked mobile platforms, such as underwater crawlers, proves beneficial, extending the surveillance radius of underwat...

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Detalles Bibliográficos
Autores: Falahzadehabarghouee, Ahmad, Toma, Daniel|||0000-0003-0472-1190, Nogueras Cervera, Marc|||0000-0001-7272-0128, Martínez Padró, Enoc|||0000-0003-1233-7105, Carandell Widmer, Matias|||0000-0003-0559-4453, Aguzzi, Jacopo, Río Fernández, Joaquín del|||0000-0002-6191-2201
Tipo de recurso: artículo
Fecha de publicación:2024
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:2117/411117
Acceso en línea:https://hdl.handle.net/2117/411117
https://dx.doi.org/10.5821/iwp.2024.23.14124
Access Level:acceso abierto
Palabra clave:Autonomous underwater vehicles
Remote submersibles
Underwater robot
Underwater crawler
Custom object tracking
Remotely operated vehicle (ROV)
Autonomous Underwater Vehicle (AUV)
Internet Operated Vehicles (IOV)
Vehicles submergibles autònoms
Vehicles submergibles remots
Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
Descripción
Sumario:The strategic advancement of monitoring protocols relies on cabled marine observatories for obtaining real-time multiparametric biological and environmental data. The integration of docked mobile platforms, such as underwater crawlers, proves beneficial, extending the surveillance radius of underwater observatories and enhancing their overall performance and functionality. Normally, underwater crawlers are controlled manually, resulting in considerable time and cost expenditures for both crawler operation and monitoring seabed species. To overcome this challenge, the OBSEA Underwater crawler was employed to detect and track a custom object in the laboratory environment. The detection process involved the preparation of a dataset, training a model with YOLO, and finally utilizing an algorithm to track the custom object.