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

Muhammad, Khan; Ahmad, Jamil; Baik, Sung Wook

doi:10.1016/j.neucom.2017.04.083

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

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 Mahdi et al.'s experiment, Mivia dataset achieved accuracy 95.32%, outperforming CNN-based system [132] that achieved 94.39%.
“For instance, the outcomes of the Mivia dataset experiment achieved an accuracy of 95.32%, outperforming another CNN-based system [132] that achieved 94.39% accuracy.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [132]

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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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