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Deep Learning-Based Fire Detection System
Subject area: Science,Engineering and Technology · Area of research: Deep Learning
Abstract
In emergency situations like fire accidents fire causes environmental disaster, economic losses and deep damages to humans. To detect fire earlier and to avoid damages caused by fire we have built a project which is deep learning-based fire detection system. With the help of deep learning algorithms such as CNN we have developed this fire detection system. In this project we have introduced a system which is trained by using images as a dataset and with the help of CNN algorithm module is trained to detect fire from provided video. Input is provided in video format the input video is converted into images with the help of CNN layers. The trained module detects fire which is trained and working with the help of coding. The deep learning-based fire detection gives output in text format as ?fire detected? and alert is given to the nearest fire station or owner of the system is alerted by sending messages. This system results into early and accurate detection of fire through video or surveillance camera. This is efficient to early detection and fire is avoided by spreading. With this approach, fire detection systems are likely to become far more precise and effective.
Keywords
Automated Fire Detection, Convolutional Neural Network (CNN), Early Fire Detection, Fire Prevention, Pattern Recognition, Neural Networks
References
[1] Ke Chen, Yanying Cheng, “Research on Image Fire Detection Based on Support Vector Machine”, (2020), IEEE
[2] Khan Muhammad Student Member, IEEE Jamil Ahmad, Student Member IEEE, Zhihan L Member IEEE Paolo Bellavista Senior Member IEEE Po Yang, Member, IEEE, and ung Wook Baik, Member, lEEE,’ efficient Deep NN-Based Fire Detection and Localization in Video Surveillance application’s' 2018 IEEE, http://www.ieee.org/publications- standard /publication /rights/index.html
[3] HUANG HONGYUl, KUANG PING I LIFANJ HI HUAXINJ "AN Ilvl PROVED MULTI - SCALE FIRE DETECTIO METHOD BASED ON CONVO LUTIO AL NEURAL NETWORK, 978-l-6654-0505-8/20/3J .00 2020 IEEE.
[4] Oxsy G.iandi, Riyanarto Sarno Prototype of Fire Symptom Detection System' 978-l-5386-0954- 5/18/3 l.00
[5] Jiang Feng, Yang Feng, ''Design and experimental research of video detection sy tern for hip fire
[6] Sneba Wil on 2Shyni P Varghe e, 3Nikhi1 G A, 4 ManoJekshmi I, 5 Raji PG, A Comprehensive Study on Fire Detection', Proc. IEEE Conference on Emerging Devices and Smart Systems (ICEDSS 2018).
[7] C. Kao and. bang • an lnteUigent Real-Time Fire-Detection Method Based on Video Processing,' IEEE 37th nun. 2003 Int. Carnahan Conf. OnSe u1ity Tecbnol 2003 Proc. 2003.
How to cite this paper
@article{1707989,
author = {Utkarsha Sanjay Pandharkar, Dipali Adhyapak},
title = {Deep Learning-Based Fire Detection System},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {933-939},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707989.pdf},
abstract = {In emergency situations like fire accidents fire causes environmental disaster, economic losses and deep damages to humans. To detect fire earlier and to avoid damages caused by fire we have built a project which is deep learning-based fire detection system. With the help of deep learning algorithms such as CNN we have developed this fire detection system. In this project we have introduced a system which is trained by using images as a dataset and with the help of CNN algorithm module is trained to detect fire from provided video. Input is provided in video format the input video is converted into images with the help of CNN layers. The trained module detects fire which is trained and working with the help of coding. The deep learning-based fire detection gives output in text format as ?fire detected? and alert is given to the nearest fire station or owner of the system is alerted by sending messages. This system results into early and accurate detection of fire through video or surveillance camera. This is efficient to early detection and fire is avoided by spreading. With this approach, fire detection systems are likely to become far more precise and effective.},
keywords = {Automated Fire Detection, Convolutional Neural Network (CNN), Early Fire Detection, Fire Prevention, Pattern Recognition, Neural Networks},
month = {April},
}