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

El 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...

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Autores:
Cano Solis, Mateo
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/85423
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/85423
https://repositorio.unal.edu.co/
Palabra clave:
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
Inteligencia artificial
Procesamiento digital de imágenes
Líneas eléctricas
GeoAI
UAV
Vegetation encroachment
Power lines
Deep learning
Semantic segmentation
Artificial intelligence
Machine learning
Drones
Invasión por vegetación
Líneas eléctricas
Aprendizaje profundo
Segmentación
Inteligencia artificial
Aprendizaje automático
Aprendizaje profundo
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional
id UNACIONAL2_e48cda840854e651cec056327ee585f9
oai_identifier_str oai:repositorio.unal.edu.co:unal/85423
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Segmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de drone
dc.title.translated.eng.fl_str_mv Segmentation of vegetation encroachment on electrical transmission lines using deep learning on drone images
title Segmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de drone
spellingShingle Segmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de drone
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
Inteligencia artificial
Procesamiento digital de imágenes
Líneas eléctricas
GeoAI
UAV
Vegetation encroachment
Power lines
Deep learning
Semantic segmentation
Artificial intelligence
Machine learning
Drones
Invasión por vegetación
Líneas eléctricas
Aprendizaje profundo
Segmentación
Inteligencia artificial
Aprendizaje automático
Aprendizaje profundo
title_short Segmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de drone
title_full Segmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de drone
title_fullStr Segmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de drone
title_full_unstemmed Segmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de drone
title_sort Segmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de drone
dc.creator.fl_str_mv Cano Solis, Mateo
dc.contributor.advisor.none.fl_str_mv Ballesteros Parra, John Robert
Branch Bedoya, John Willian
dc.contributor.author.none.fl_str_mv Cano Solis, Mateo
dc.contributor.researchgroup.spa.fl_str_mv Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial
dc.contributor.orcid.spa.fl_str_mv https://orcid.org/0000-0001-9988-4624
dc.contributor.cvlac.spa.fl_str_mv https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001689779
dc.contributor.scopus.spa.fl_str_mv 58551783500
dc.contributor.researchgate.spa.fl_str_mv https://www.researchgate.net/profile/Mateo-Cano-Solis
dc.contributor.googlescholar.spa.fl_str_mv https://scholar.google.com/citations?user=OkGRZ30AAAAJ&hl=es
dc.subject.ddc.spa.fl_str_mv 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
topic 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
Inteligencia artificial
Procesamiento digital de imágenes
Líneas eléctricas
GeoAI
UAV
Vegetation encroachment
Power lines
Deep learning
Semantic segmentation
Artificial intelligence
Machine learning
Drones
Invasión por vegetación
Líneas eléctricas
Aprendizaje profundo
Segmentación
Inteligencia artificial
Aprendizaje automático
Aprendizaje profundo
dc.subject.lemb.none.fl_str_mv Inteligencia artificial
Procesamiento digital de imágenes
Líneas eléctricas
dc.subject.proposal.eng.fl_str_mv GeoAI
UAV
Vegetation encroachment
Power lines
Deep learning
Semantic segmentation
Artificial intelligence
Machine learning
dc.subject.proposal.spa.fl_str_mv Drones
Invasión por vegetación
Líneas eléctricas
Aprendizaje profundo
Segmentación
Inteligencia artificial
Aprendizaje automático
dc.subject.wikidata.none.fl_str_mv Aprendizaje profundo
description El 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)
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-01-24T19:52:16Z
dc.date.available.none.fl_str_mv 2024-01-24T19:52:16Z
dc.date.issued.none.fl_str_mv 2024-01-14
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/85423
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/85423
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.indexed.spa.fl_str_mv LaReferencia
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dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Medellín - Minas - Maestría en Ingeniería - Analítica
dc.publisher.faculty.spa.fl_str_mv Facultad de Minas
dc.publisher.place.spa.fl_str_mv Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Medellín
institution Universidad Nacional de Colombia
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spelling Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ballesteros Parra, John Robert8b10cddb5010474c877da0b0b9ef76faBranch Bedoya, John Willian112eaa0bbeeaeb0d3d14dfe15d672a15600Cano Solis, Mateof8bc8e8cf253d76847c9e0d93ba4fb35Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificialhttps://orcid.org/0000-0001-9988-4624https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=000168977958551783500https://www.researchgate.net/profile/Mateo-Cano-Solishttps://scholar.google.com/citations?user=OkGRZ30AAAAJ&hl=es2024-01-24T19:52:16Z2024-01-24T19:52:16Z2024-01-14https://repositorio.unal.edu.co/handle/unal/85423Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/El 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)The 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.MaestríaMagister en Ingeniería - AnalíticaGEOAIDeep LearningÁrea Curricular de Ingeniería de Sistemas e Informática47 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computaciónInteligencia artificialProcesamiento digital de imágenesLíneas eléctricasGeoAIUAVVegetation encroachmentPower linesDeep learningSemantic segmentationArtificial intelligenceMachine learningDronesInvasión por vegetaciónLíneas eléctricasAprendizaje profundoSegmentaciónInteligencia artificialAprendizaje automáticoAprendizaje profundoSegmentación de la invasión por vegetación a líneas de transmisión eléctrica usando aprendizaje profundo en imágenes de droneSegmentation of vegetation encroachment on electrical transmission lines using deep learning on drone imagesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMLaReferenciaV. 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Remote Sens., vol. 59, no. 7, pp. 6169–6181, 2021, doi: 10.1109/TGRS.2020.3026051.EstudiantesInvestigadoresMedios de comunicaciónPúblico generalReceptores de fondos federales y solicitantesLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85423/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1037660293.2024.pdf1037660293.2024.pdfTesis de Maestría en Ingeniería - Analíticaapplication/pdf1274413https://repositorio.unal.edu.co/bitstream/unal/85423/4/1037660293.2024.pdf84cd4db07f36e4975328d9fc31d1ec28MD54THUMBNAIL1037660293.2024.pdf.jpg1037660293.2024.pdf.jpgGenerated Thumbnailimage/jpeg3506https://repositorio.unal.edu.co/bitstream/unal/85423/5/1037660293.2024.pdf.jpgd03470412faa5df8cda0b61c921b14beMD55unal/85423oai:repositorio.unal.edu.co:unal/854232024-08-21 23:13:04.827Repositorio Institucional Universidad Nacional de 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