Object detection for single tree species identification with high resolution aerial images

Detalhes bibliográficos
Autor(a) principal: Boyagoda, Ekanayaka Mudiyanse Ralahamilage Chamodi Lakmali
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/93643
Resumo: Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
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spelling Object detection for single tree species identification with high resolution aerial imagesConvolutional Neural NetworkHigh Resolution Aerial ImagesImage ClassificationObject DetectionRegion-based Convolutional Neural NetworkRemote SensingUnnamed Aerial VehicleDissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesObject recognition is one of the computer vision tasks developing rapidly with the invention of Region-based Convolutional Neural Network (RCNN). This thesis contains a study conducted using RCNN base object detection technique to identify palm trees in three datasets having RGB images taken by Unnamed Aerial Vehicles (UAVs). The method was entirely implemented using TensorFlow object detection API to compare the performance of pre-trained faster RCNN object detection models. According to the results, best performance was recorded with the highest overall accuracy of 93.1 ± 4.5 % and the highest speed of 9m 57s from faster RCNN model which was having inceptionv2 as feature extractor. The poorest performance was recorded with the lowest overall accuracy of 65.2 ± 10.9% and the lowest speed of 5h 39m 15s from faster RCNN model which was having inception_resnetv2 as feature extractor.Silva, Joel Dinis Baptista Ferreira daCabral, Pedro da Costa BritoPla Bañón, FilibertoRUNBoyagoda, Ekanayaka Mudiyanse Ralahamilage Chamodi Lakmali2020-03-02T18:27:32Z2020-02-272020-02-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/93643TID:202456897enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T17:43:48Zoai:run.unl.pt:10362/93643Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:43:48Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Object detection for single tree species identification with high resolution aerial images
title Object detection for single tree species identification with high resolution aerial images
spellingShingle Object detection for single tree species identification with high resolution aerial images
Boyagoda, Ekanayaka Mudiyanse Ralahamilage Chamodi Lakmali
Convolutional Neural Network
High Resolution Aerial Images
Image Classification
Object Detection
Region-based Convolutional Neural Network
Remote Sensing
Unnamed Aerial Vehicle
title_short Object detection for single tree species identification with high resolution aerial images
title_full Object detection for single tree species identification with high resolution aerial images
title_fullStr Object detection for single tree species identification with high resolution aerial images
title_full_unstemmed Object detection for single tree species identification with high resolution aerial images
title_sort Object detection for single tree species identification with high resolution aerial images
author Boyagoda, Ekanayaka Mudiyanse Ralahamilage Chamodi Lakmali
author_facet Boyagoda, Ekanayaka Mudiyanse Ralahamilage Chamodi Lakmali
author_role author
dc.contributor.none.fl_str_mv Silva, Joel Dinis Baptista Ferreira da
Cabral, Pedro da Costa Brito
Pla Bañón, Filiberto
RUN
dc.contributor.author.fl_str_mv Boyagoda, Ekanayaka Mudiyanse Ralahamilage Chamodi Lakmali
dc.subject.por.fl_str_mv Convolutional Neural Network
High Resolution Aerial Images
Image Classification
Object Detection
Region-based Convolutional Neural Network
Remote Sensing
Unnamed Aerial Vehicle
topic Convolutional Neural Network
High Resolution Aerial Images
Image Classification
Object Detection
Region-based Convolutional Neural Network
Remote Sensing
Unnamed Aerial Vehicle
description Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
publishDate 2020
dc.date.none.fl_str_mv 2020-03-02T18:27:32Z
2020-02-27
2020-02-27T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/93643
TID:202456897
url http://hdl.handle.net/10362/93643
identifier_str_mv TID:202456897
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
_version_ 1817545729661468672

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