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<dim:field authority="55b9a52a-a549-48dd-8616-3893ca99e817" element="contributor" qualifier="advisor" confidence="UNCERTAIN" language="es-ES" mdschema="dc">gonzalez Santander de la Cruz, guillermo</dim:field>
<dim:field authority="5aba3dfc-a980-4ee8-9c83-999433d5b7ef" element="contributor" qualifier="author" confidence="UNCERTAIN" language="es-ES" mdschema="dc">Valle Gutiérrez, Guillermo</dim:field>
<dim:field element="contributor" qualifier="other" language="es_ES" mdschema="dc">Universidad Pontificia Comillas, Escuela Técnica Superior de Ingeniería (ICAI)</dim:field>
<dim:field element="date" qualifier="accessioned" mdschema="dc">2025-05-01T21:12:25Z</dim:field>
<dim:field element="date" qualifier="available" mdschema="dc">2025-05-01T21:12:25Z</dim:field>
<dim:field element="date" qualifier="issued" language="es_ES" mdschema="dc">2025</dim:field>
<dim:field element="identifier" qualifier="uri" mdschema="dc">http://hdl.handle.net/11531/98622</dim:field>
<dim:field element="description" language="es_ES" mdschema="dc">Máster Universitario en Big Data</dim:field>
<dim:field element="description" qualifier="abstract" language="es-ES" mdschema="dc">El presente trabajo presenta el desarrollo de un sistema basado en técnicas de visión artificial y Deep Learning para gestión del inventario de activos en redes de distribución eléctrica. Para ello, se ha utilizado un modelo de detección open-set (Grounding DINO) y clasificadores específicos realizando un fine-tuning de los modelos (YOLOv8). Obteniendo una herramienta capaz de identificar visualmente postes eléctricos y clasificarlos en función de características técnicas como material y función. Los resultados obtenidos muestran un alto rendimiento en las tareas de detección y clasificación, validando la viabilidad de aplicar este enfoque en el sector energético.</dim:field>
<dim:field element="description" qualifier="abstract" language="en-GB" mdschema="dc">This paper presents the development of a system based on artificial vision and Deep Learning techniques for asset inventory management in electricity distribution networks. For this purpose, an open-set detection model (Grounding DINO) and specific classifiers have been used, performing a fine-tuning of the models (YOLOv8). A tool capable of visually identifying electric pylons and classifying them according to technical characteristics such as material and function has been obtained. The results obtained show a high performance in the detection and classification tasks, validating the feasibility of applying this approach in the energy sector.</dim:field>
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<dim:field element="rights" language="es_ES" mdschema="dc">Attribution-NonCommercial-NoDerivs 3.0 United States</dim:field>
<dim:field element="rights" qualifier="uri" language="es_ES" mdschema="dc">http://creativecommons.org/licenses/by-nc-nd/3.0/us/</dim:field>
<dim:field element="subject" qualifier="other" language="es_ES" mdschema="dc">H0Z</dim:field>
<dim:field element="title" language="es_ES" mdschema="dc">Desarrollo de un sistema basado en visión artificial para la supervisión de redes de distribución.</dim:field>
<dim:field element="type" language="es_ES" mdschema="dc">info:eu-repo/semantics/masterThesis</dim:field>
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<dim:field element="keywords" language="es-ES" mdschema="dc">poste eléctrico, YOLOv8, Grounding DINO, visión artificial, Deep Learning, inventario de activos, redes de distribución</dim:field>
<dim:field element="keywords" language="en-GB" mdschema="dc">electric pylon, YOLOv8, Grounding DINO, artificial vision, Deep Learning, asset inventory, distribution networks</dim:field>
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