Segmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de drone

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dc.contributor.advisorBallesteros Parra, John Robert
dc.contributor.advisorBranch Bedoya, John Willian
dc.contributor.authorCano Solis, Mateo
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001689779spa
dc.contributor.googlescholarhttps://scholar.google.com/citations?user=OkGRZ30AAAAJ&hl=esspa
dc.contributor.orcidhttps://orcid.org/0000-0001-9988-4624spa
dc.contributor.researchgatehttps://www.researchgate.net/profile/Mateo-Cano-Solisspa
dc.contributor.researchgroupGidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificialspa
dc.contributor.scopus58551783500spa
dc.date.accessioned2024-01-24T19:52:16Z
dc.date.available2024-01-24T19:52:16Z
dc.date.issued2024-01-14
dc.description.abstractEl trabajo de tesis se enfoca en abordar el problema de los cortes de energía en líneas de transmisión eléctrica debido a la invasión de vegetación. Se propone un enfoque basado en el uso de imágenes de drones y técnicas de aprendizaje profundo para anticipar y detectar la presencia de vegetación en estas líneas. El objetivo general es desarrollar un flujo de trabajo que permita segmentar áreas invadidas por vegetación, mediante la creación de un conjunto de datos público de imágenes de drones, la preparación y fusión de datos, y la selección de una arquitectura de aprendizaje profundo para la detección. El método propuesto se presenta como una alternativa más eficiente y confiable en comparación con los métodos tradicionales de revisión manual en campo. El enfoque busca proporcionar una herramienta efectiva para la detección temprana de invasión de vegetación, contribuyendo así a mejorar la calidad y confiabilidad del suministro eléctrico y reduciendo los costos asociados a los cortes de energía generados por este problema. (Tomado de la fuente)spa
dc.description.abstractThe thesis work focuses on addressing the issue of power outages in electrical transmission lines caused by vegetation encroachment. An approach is proposed that relies on drone imagery and deep learning techniques to anticipate and detect vegetation invasion in these lines. The overall objective is to develop a workflow that allows for the segmentation of vegetation-invaded areas, achieved through the creation of a public dataset of drone images, data preparation and fusion strategies, and the selection of a deep learning architecture for detection. The proposed method is presented as a more efficient and reliable alternative compared to traditional manual field inspection methods. The approach aims to provide an effective tool for early detection of vegetation encroachment, thereby contributing to enhancing the quality and reliability of the electrical power supply and reducing costs associated with power outages caused by this problem.eng
dc.description.curricularareaÁrea Curricular de Ingeniería de Sistemas e Informáticaspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagister en Ingeniería - Analíticaspa
dc.description.researchareaGEOAIspa
dc.description.researchareaDeep Learningspa
dc.format.extent47 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/85423
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellínspa
dc.publisher.facultyFacultad de Minasspa
dc.publisher.placeMedellín, Colombiaspa
dc.publisher.programMedellín - Minas - Maestría en Ingeniería - Analíticaspa
dc.relation.indexedLaReferenciaspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónspa
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computaciónspa
dc.subject.lembInteligencia artificial
dc.subject.lembProcesamiento digital de imágenes
dc.subject.lembLíneas eléctricas
dc.subject.proposalGeoAIeng
dc.subject.proposalDronesspa
dc.subject.proposalInvasión por vegetaciónspa
dc.subject.proposalLíneas eléctricasspa
dc.subject.proposalAprendizaje profundospa
dc.subject.proposalSegmentaciónspa
dc.subject.proposalInteligencia artificialspa
dc.subject.proposalAprendizaje automáticospa
dc.subject.proposalUAVeng
dc.subject.proposalVegetation encroachmenteng
dc.subject.proposalPower lineseng
dc.subject.proposalDeep learningeng
dc.subject.proposalSemantic segmentationeng
dc.subject.proposalArtificial intelligenceeng
dc.subject.proposalMachine learningeng
dc.subject.wikidataAprendizaje profundo
dc.titleSegmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de dronespa
dc.title.translatedSegmentation of vegetation encroachment on electrical transmission lines using deep learning on drone imageseng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMedios de comunicaciónspa
dcterms.audience.professionaldevelopmentPúblico generalspa
dcterms.audience.professionaldevelopmentReceptores de fondos federales y solicitantesspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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