A hybrid method for fire detection based on spatial and temporal patterns

Página do item simplificado
dc.creatorPedro Vinícius A. B. de Venâncio
dc.creatorRoger Junio Campos
dc.creatorTamires M. Rezende
dc.creatorAdriano C. Lisboa
dc.creatorAdriano V. Barbosa
dc.date.accessioned2025-05-29T13:56:21Z
dc.date.accessioned2025-09-09T01:33:05Z
dc.date.available2025-05-29T13:56:21Z
dc.date.issued2023
dc.identifier.doi10.1007/s00521-023-08260-2
dc.identifier.issn09410643
dc.identifier.urihttps://hdl.handle.net/1843/82614
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.rightsAcesso Restrito
dc.subjectIncêndios florestais
dc.subject.otherdeep convolutional neural network
dc.subject.otherhybrid wildfire detection method
dc.subject.othertemporal analysis
dc.subject.otherAccording to Brazil’s National Institute for Space Research (INPE), approximately 600,338 fires were recorded in South America in 2021 and the first 10 months of 2022. Of these, about 338,580 cases were in Brazil. A system that enables early fire detection followed by immediate intervention has the potential to significantly decrease such high numbers, reducing the damage caused to the environment.
dc.titleA hybrid method for fire detection based on spatial and temporal patterns
dc.typeArtigo de periódico
local.citation.spage1
local.citation.volume1
local.description.resumoFire detection is a vital task for social, economic and environmental reasons. Early identification of fire outbreaks is crucial in order to limit the damage that will be sustained. In open areas, this task is typically performed by humans, e.g., security guards, who are responsible for watching out for possible occurrences. However, people may get distracted, or may not have enough eyesight, which can result in considerable delays in identifying a fire, after much damage has occurred. Thus, the idea of having machines to automatically detect fires has long been considered an interesting possibility. Over the years, different approaches for fire detection have been developed using computer vision. Currently, the most promising ones are based on convolutional neural networks (CNNs). However, smoke and fire, the main visual indicators of wildfires, present additional difficulties for the vast majority of such learning systems. Both smoke and fire have a high intra-class variance, assuming different shapes, colors and textures, which makes the learning process more complicated than for well-defined objects. This work proposes an automatic fire detection method based on both spatial (visual) and temporal patterns. This hybrid method works in two stages: (i) detection of probable fire events by a CNN based on visual patterns (spatial processing) and (ii) analysis of the dynamics of these events over time (temporal processing). Experiments performed on our surveillance video database show that cascading these two stages can reduce the false positive rate with no significant impact either on the true positive rate or the processing time.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://link.springer.com/article/10.1007/s00521-023-08260-2

Arquivos

Licença do pacote

Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
License.txt
Tamanho:
1.99 KB
Formato:
Plain Text
Descrição:
Baixar

Coleções

Artigo de Periódico

Logo UFMG