Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks

Fire is one of the most commonly occurring disasters and is the main cause of catastrophic personal injury and devastating property damage. An early detection system is necessary to prevent fires from spreading out of control. In this paper, we propose a multistage fire detection method using convol...

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Autores principales:	Manh Dung Nguyen, Hoai Nam Vu, Duc Cuong Pham, Bokgil Choi, Soonghwan Ro
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Fire detection convolutional neural network ImageNet long short-term memory Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/acf21ac3520745d5bfa8161573b7ecbe
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spelling	oai:doaj.org-article:acf21ac3520745d5bfa8161573b7ecbe2021-11-09T00:02:27ZMultistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks2169-353610.1109/ACCESS.2021.3122346https://doaj.org/article/acf21ac3520745d5bfa8161573b7ecbe2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9584840/https://doaj.org/toc/2169-3536Fire is one of the most commonly occurring disasters and is the main cause of catastrophic personal injury and devastating property damage. An early detection system is necessary to prevent fires from spreading out of control. In this paper, we propose a multistage fire detection method using convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. In the first stage, fire candidates are detected by using their salient features, such as their color, flickering frequency, and brightness. In the second stage, a pretrained CNN model is used to extract the 2D features of flames that are the input for the LSTM network. In the last stage, a softmax classifier is utilized to determine whether the flames represent a true fire or a nonfire moving object. The experimental results show that our proposed method can achieve competitive performance compared with other state-of-the-art methods and is suitable for real-world applications.Manh Dung NguyenHoai Nam VuDuc Cuong PhamBokgil ChoiSoonghwan RoIEEEarticleFire detectionconvolutional neural networkImageNetlong short-term memoryElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146667-146679 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fire detection convolutional neural network ImageNet long short-term memory Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Fire detection convolutional neural network ImageNet long short-term memory Electrical engineering. Electronics. Nuclear engineering TK1-9971 Manh Dung Nguyen Hoai Nam Vu Duc Cuong Pham Bokgil Choi Soonghwan Ro Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks
description	Fire is one of the most commonly occurring disasters and is the main cause of catastrophic personal injury and devastating property damage. An early detection system is necessary to prevent fires from spreading out of control. In this paper, we propose a multistage fire detection method using convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. In the first stage, fire candidates are detected by using their salient features, such as their color, flickering frequency, and brightness. In the second stage, a pretrained CNN model is used to extract the 2D features of flames that are the input for the LSTM network. In the last stage, a softmax classifier is utilized to determine whether the flames represent a true fire or a nonfire moving object. The experimental results show that our proposed method can achieve competitive performance compared with other state-of-the-art methods and is suitable for real-world applications.
format	article
author	Manh Dung Nguyen Hoai Nam Vu Duc Cuong Pham Bokgil Choi Soonghwan Ro
author_facet	Manh Dung Nguyen Hoai Nam Vu Duc Cuong Pham Bokgil Choi Soonghwan Ro
author_sort	Manh Dung Nguyen
title	Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks
title_short	Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks
title_full	Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks
title_fullStr	Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks
title_full_unstemmed	Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks
title_sort	multistage real-time fire detection using convolutional neural networks and long short-term memory networks
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/acf21ac3520745d5bfa8161573b7ecbe
work_keys_str_mv	AT manhdungnguyen multistagerealtimefiredetectionusingconvolutionalneuralnetworksandlongshorttermmemorynetworks AT hoainamvu multistagerealtimefiredetectionusingconvolutionalneuralnetworksandlongshorttermmemorynetworks AT duccuongpham multistagerealtimefiredetectionusingconvolutionalneuralnetworksandlongshorttermmemorynetworks AT bokgilchoi multistagerealtimefiredetectionusingconvolutionalneuralnetworksandlongshorttermmemorynetworks AT soonghwanro multistagerealtimefiredetectionusingconvolutionalneuralnetworksandlongshorttermmemorynetworks
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Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks

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