A deep learning based object identification system for forest fire detection

Detalhes bibliográficos
Autor(a) principal: Guede-Fernández, Federico
Data de Publicação: 2021
Outros Autores: Martins, Leonardo, de Almeida, Rui Valente, Gamboa, Hugo, Vieira, Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/132721
Resumo: POCI-01-0247-FEDER-038342
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spelling A deep learning based object identification system for forest fire detectionDeep learningFire detectionSmoke detectionWildfiresForestryBuilding and ConstructionSafety, Risk, Reliability and QualityEnvironmental Science (miscellaneous)Safety ResearchEarth and Planetary Sciences (miscellaneous)POCI-01-0247-FEDER-038342Forest fires are still a large concern in several countries due to the social, environmental and economic damages caused. This paper aims to show the design and validation of a proposed system for the classification of smoke columns with object detection and a deep learning-based approach. This approach is able to detect smoke columns visible below or above the horizon. During the dataset labelling, the smoke object was divided into three different classes, depending on its distance to the horizon, a cloud object was also added, along with images without annotations. A comparison between the use of RetinaNet and Faster R-CNN was also performed. Using an independent test set, an F1-score around 80%, a G-mean around 80% and a detection rate around 90% were achieved by the two best models: both were trained with the dataset labelled with three different smoke classes and with augmentation; Faster R-CNNN was the model architecture, re-trained during the same iterations but following different learning rate schedules. Finally, these models were tested in 24 smoke sequences of the public HPWREN dataset, with 6.3 min as the average time elapsed from the start of the fire compared to the first detection of a smoke column.LIBPhys-UNLDF – Departamento de FísicaRUNGuede-Fernández, FedericoMartins, Leonardode Almeida, Rui ValenteGamboa, HugoVieira, Pedro2022-02-10T23:19:07Z2021-122021-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/132721engPURE: 36699596https://doi.org/10.3390/fire4040075info: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:RCAAP2024-03-11T05:11:23Zoai:run.unl.pt:10362/132721Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:34.380488Repositório 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 A deep learning based object identification system for forest fire detection
title A deep learning based object identification system for forest fire detection
spellingShingle A deep learning based object identification system for forest fire detection
Guede-Fernández, Federico
Deep learning
Fire detection
Smoke detection
Wildfires
Forestry
Building and Construction
Safety, Risk, Reliability and Quality
Environmental Science (miscellaneous)
Safety Research
Earth and Planetary Sciences (miscellaneous)
title_short A deep learning based object identification system for forest fire detection
title_full A deep learning based object identification system for forest fire detection
title_fullStr A deep learning based object identification system for forest fire detection
title_full_unstemmed A deep learning based object identification system for forest fire detection
title_sort A deep learning based object identification system for forest fire detection
author Guede-Fernández, Federico
author_facet Guede-Fernández, Federico
Martins, Leonardo
de Almeida, Rui Valente
Gamboa, Hugo
Vieira, Pedro
author_role author
author2 Martins, Leonardo
de Almeida, Rui Valente
Gamboa, Hugo
Vieira, Pedro
author2_role author
author
author
author
dc.contributor.none.fl_str_mv LIBPhys-UNL
DF – Departamento de Física
RUN
dc.contributor.author.fl_str_mv Guede-Fernández, Federico
Martins, Leonardo
de Almeida, Rui Valente
Gamboa, Hugo
Vieira, Pedro
dc.subject.por.fl_str_mv Deep learning
Fire detection
Smoke detection
Wildfires
Forestry
Building and Construction
Safety, Risk, Reliability and Quality
Environmental Science (miscellaneous)
Safety Research
Earth and Planetary Sciences (miscellaneous)
topic Deep learning
Fire detection
Smoke detection
Wildfires
Forestry
Building and Construction
Safety, Risk, Reliability and Quality
Environmental Science (miscellaneous)
Safety Research
Earth and Planetary Sciences (miscellaneous)
description POCI-01-0247-FEDER-038342
publishDate 2021
dc.date.none.fl_str_mv 2021-12
2021-12-01T00:00:00Z
2022-02-10T23:19:07Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/132721
url http://hdl.handle.net/10362/132721
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 36699596
https://doi.org/10.3390/fire4040075
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
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