Cited by 558Fire Detection and Safety SystemsAdvanced Neural Network ApplicationsFire dynamics and safety research

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Early fire detection using convolutional neural networks during surveillance for effective disaster management

Muhammad, Khan, Ahmad, Jamil, Baik, Sung Wook·2018·Neurocomputing

DOI 10.1016/j.neucom.2017.04.083

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“In addition, [9] presents a fire detection framework for CCTV monitoring that emphasizes improved accuracy by employing adaptive prioritizing.”
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“Human activity recognition (HAR) is one of the popular studied computer vision applications including video surveillance [1][2] and health care [3]-[5] which includes elderly falling activity recognition [6]-[11].”
Background
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“Muhammad [12] inputs the images into the pre-trained AlexNet without pre-processing, his method only solves the classification of flame and nonflame, and does not locate the flame position.”
Method
Application of Big Data and Machine Learning Approaches to Improve Decision Making During Crises
Ali, M. (2022) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-7998-9815-3.ch005 · Cited by 1
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“Through the intelligent surveillance system [2], fire can be detected and located accurately and in time, and relevant departments can be automatically notified [3].”
Background
A PROPOSED FIRE DETECTION SURVELLIANCE THROUGH MACHINE LEARNING BASED ON HAAR CASCADE CLASSIFIER
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“In Ref [8] the faster R-CNN (regional CNN) was used to detect forest fire, and in ref [9] the CNN was used to early detect fire in varying indoor and outdoor environments for CCTV surveillance cameras.”
Method
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“Research conducted by Muhammad uses convolutional neural networks to detect fire through CCTV camera surveillance [8].”
Method
Fire detection using video sequences in urban out-door environment
zarchi, F. K., Derhami, V., Latif, A., & ebrahimi, A. (2019) · Signal and Data Processing · doi:10.29252/jsdp.16.3.78
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“In recent years, cameras combining with deep learning algorithm and ZigBee networks are popular in civil engineering for a variety of applications [2][3][4].”
Background
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Baig, M. A., Khan, D. N. A., & Rafi, M. M. (2021) · IETE Journal of Research · doi:10.1080/03772063.2021.1992303
“Muhammad et al [22] proposed a framework for early fire detection to reduce the damages.”
Background
Application of Wireless Sensor Networks to Prevent the Spread of Forest Fires
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Comment on essd-2022-394
Wang, M., Jiang, L., Yue, P., et al. (2022) · doi:10.5194/essd-2022-394-rc1
Research on Flame Recognition Algorithm
Shaobo, X., & Hu, B. (2022) · Advances in transdisciplinary engineering · doi:10.3233/atde221176
Comment on essd-2022-394
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2022-394-rc2
Comment on essd-2022-394
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2022-394-rc3
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“Whereas techniques such as patrolling and watching towers are simple and achievable but aren't particularly successful since they need a large investment of time and money as well as a skilled workforce [7].”
Background
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc1
Reply on CC1
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac1
Reply on RC1
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac2
Reply on RC1
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac3
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-rc1
Reply on RC1
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac4
Request for Dataset for my Thesis on Fire Detection
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc2
Reply on CC2
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc3
Reply on RC1
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac5
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc4
Surveillance and detection of forest fire using augmented hybrid CNN algorithm
Sruthi, S., & Anuradha, B. (2023) · AIP conference proceedings · doi:10.1063/5.0133346
Reply on CC4
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac6
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc5
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc6
Reply on CC7
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac9
dataset access
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc7
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Saxena, V., Jain, Y., & Mittal, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4500695
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc8
Reply on CC8
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac10
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc9
Image-Based Inspection System of Firefighting Facilities Using Augmented Reality
Oh, D.-S., Kim, J.-K., Lee, K., & Hong, S. G. (2023) · Fire science and engineering · doi:10.7731/kifse.05a96a47
Reply on CC9
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac12
Reply on CC10
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac13
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc10
Spain on Fire: A Novel Wildfire Risk Assessment Model Based on Image Satellite Processing and Atmospheric InformationPreprint
Liz, H., Huertas‐Tato, J., Pérez‐Aracil, J., Casanova-Mateo, C., Sanz, J., & Camacho, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4504356
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc11
Reply on CC11
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac14
Forest Fire Detection using Nine-Layer Deep Convolutional Neural Network
Sethy, P. K., Devi, A. G., Sahoo, P. K., Nath, A. K., Sahoo, T., & Patra, S. S. (2023) · International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct56650.2023.10179789
Reply on CC12
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac15
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-cc12
Comment on essd-2023-73
Wang, M., Jiang, L., Yue, P., et al. (2023) · doi:10.5194/essd-2023-73-rc2
A Novel Features Selection Model for Fire Detection and Fire Circumstances Recognition by Considering Fire Texture: MIC-RF-RFE
Jetwiriyanon, J., Feng, Z., & Wattanachote, K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-37717-4_71
Determining the features of histograms of dangerous parameters of the gas environment in the absence and occurrence of fire
Pospelov, B., Rybka, E., Bezuhla, Y., Khalmuradov, B., Petukhova, O., Gornostal, S., Kozar, Y., Яцентюк, Ю. В., Hryshko, S., & Manzhura, S. (2023) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2023.285966
“The study of the degree of efficiency and accuracy of detection of SP based on BT was carried out in [24, 27].”
BackgroundMethod
Reply on RC2
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac16
Reply on RC1
Wang, M. (2023) · doi:10.5194/essd-2023-73-ac17
Utilização eficiente em larga escala de reconhecimento facial para análise preditiva de segurança em cidades inteligentes.
da Silva, M. A. (2019) · doi:10.11606/d.3.2019.tde-26102023-101641
Dynamics of skewness and kurtosis of dangerous environmental parameters in the event of fire
Pospelov, B., Andronov, V., Bezuhla, Y., Lukysha, R., Lutsenko, T., Kozar, Y., Kravtsov, M., Гула, Л., Непша, О., & Zavialova, T. (2023) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2023.288938
“However, it is known that the harbinger of any Fs in the room is the accidental ignition (IG) of the material [24].”
BackgroundMethod
Comparative Study of Image-Based Fire Detection Algorithms
Jain, S., Narayan, S., Antoo, T. N., & Jindal, R. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10306529
Fire Detection System for Urban Structures with Integrated use of Multisensors in Embodied Technology
do Nascimento, M. G., Júnior, R. A. D. M., de Souza, B. B., Gonçalves, C. S., & Santos, M. D. O. (2023) · Revista de Gestão Social e Ambiental · doi:10.24857/rgsa.v18n2-028
Unsupervised Optical Flow Estimation Model with Multi-Scale Overlapping Partition for Fire Object DetectionPreprint
Wu, Y., Kan, S., Cen, L., Xie, Y., & Chen, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4656384
Sistem Pendeteksi Api Dini Berbasis Raspberry Pi Termonitoring Aplikasi Telegram
Prakoso, B. A., Pahlevi, R. R., & Shtevanie, F. (2023) · LOGIC Jurnal Penelitian Informatika · doi:10.25124/logic.v1i1.6450
“Model YOLOv5 merupakan salah satu model pembelajaran mesin yang digunakan untuk objek deteksi, yang dapat mengidentifikasi objek dalam gambar dengan aku-rasi yang tinggi [16].”
An approximation algorithm for bus evacuation problem
Feng, Y., Cao, Y., Yang, S., Yang, L., & Wei, T. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127252
Innovative Visual Analysis Solutions to Support Disaster Management
Michail, E., Giannakeris, P., Koulalis, I., Vrochidis, S., & Kompatsiaris, I. Y. (2024) · doi:10.1002/9781119741374.ch10
Research on real-time fire detection and locating for automotive firefighting robot in factories based on Convolutional Neural Network
Tan, H. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/34/20230301
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“From the existing architecture, a PAN neck to aggregates features from the lower levels of the backbone, and then an FPN neck aggregate features from the higher levels of the backbone [9] .”
Background

Authors: Muhammad, K., Ahmad, J., & Baik, S. W.
Affiliations: Sejong University (KR)
Corresponding author: Baik, Sung Wook
ORCID: Muhammad, Khan
Ahmad, Jamil
Baik, Sung Wook
Venue: Neurocomputing
Publisher: Elsevier BV
Published: 2018-05
Volume: 288
Pages: 30-42
ISSN: 0925-2312 (ISSN-L)
DOI: 10.1016/j.neucom.2017.04.083
OpenAlex: W2780222614
Cited by: 558
References: 68
Citations by year: 2026: 40 · 2025: 62 · 2024: 78 · 2023: 120 · 2022: 87 · 2021: 60 · 2020: 51 · 2019: 43 · 2018: 17
FWCI: 44.36
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Neurocomputing (publishedVersion)
Funders: National Research Foundation of Korea (NRF) (2016R1A2B4011712)
Topics: Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Fire Detection and Safety Systems
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Fire dynamics and safety research
Keywords: early fire detection, CCTV surveillance, convolutional neural networks, fine-tuning, disaster management, dynamic channel selection, cognitive radio networks, autonomous response

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Early fire detection using convolutional neural networks during surveillance for effective disaster management

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