Convolutional Neural Networks Based Fire Detection in Surveillance Videos
Open accessAbstract
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.
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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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Self-citation
- 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
- gold
- doi.org
- 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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