Cited by 556Fire Detection and Safety SystemsAdvanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

Convolutional Neural Networks Based Fire Detection in Surveillance Videos

Open access
Muhammad, Khan, Ahmad, Jamil, Mehmood, Irfan, Rho, Seungmin, Baik, Sung Wook·2018·IEEE Access

Abstract

The recent advances in embedded processing have enabled the vision based systems to detect fire during surveillance using convolutional neural networks (CNNs). However, such methods generally need more computational time and memory, restricting its implementation in surveillance networks. In this research paper, we propose a cost-effective fire detection CNN architecture for surveillance videos. The model is inspired from GoogleNet architecture, considering its reasonable computational complexity and suitability for the intended problem compared to other computationally expensive networks such as AlexNet. To balance the efficiency and accuracy, the model is fine-tuned considering the nature of the target problem and fire data. Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in CCTV surveillance systems compared to state-of-the-art methods.

Integrity signals

Registry

Crossref · 10.1109/access.2018.2812835

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38 of 38 references verified0 citation errors · 0 not checkable

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  • A DCNN model inspired by GoogleNet architecture designed to detect both flames and smoke achieved 65% success rate in detecting smoke.

    “In [134], a DCNN model approach inspired by the GoogleNet architecture was designed to detect both flames and smoke, and it achieved a 65% success rate in detecting smoke.”

    From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [134]

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Authors
Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., & Baik, S. W.
Affiliations
  • Sejong University (KR)
  • Sungkyul University (KR)
Venue
IEEE Access
Published
2018
Volume
6
Pages
18174-18183
ISSN
2169-3536 (ISSN-L)
OpenAlex
W2793947836
Cited by
556
References
38
Citations by year
2026: 35 · 2025: 56 · 2024: 66 · 2023: 108 · 2022: 95 · 2021: 69 · 2020: 58 · 2019: 54 · 2018: 15
FWCI
59.34
Type
journal article
Language
en
Open access
Locations
  • IEEE Access (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Research Foundation of Korea (NRF)
  • Korea government (MSIP) (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 › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Keywords
fire detection, surveillance video, convolutional neural networks, GoogLeNet, lightweight CNN architecture, computational efficiency, CCTV, embedded vision, fine-tuning

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