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Forest Fire Detection Using Convolutional Neural Networks

Shruthi G1 , Disha Bhat2 , Gagana H3 , Dimple M K4 , Kavitha 5

Section:Research Paper, Product Type: Journal Paper
Volume-07 , Issue-14 , Page no. 323-325, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si14.323325

Online published on May 15, 2019

Copyright © Shruthi G, Disha Bhat, Gagana H, Dimple M K, Kavitha . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Shruthi G, Disha Bhat, Gagana H, Dimple M K, Kavitha, “Forest Fire Detection Using Convolutional Neural Networks,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.323-325, 2019.

MLA Style Citation: Shruthi G, Disha Bhat, Gagana H, Dimple M K, Kavitha "Forest Fire Detection Using Convolutional Neural Networks." International Journal of Computer Sciences and Engineering 07.14 (2019): 323-325.

APA Style Citation: Shruthi G, Disha Bhat, Gagana H, Dimple M K, Kavitha, (2019). Forest Fire Detection Using Convolutional Neural Networks. International Journal of Computer Sciences and Engineering, 07(14), 323-325.

BibTex Style Citation:
@article{G_2019,
author = {Shruthi G, Disha Bhat, Gagana H, Dimple M K, Kavitha},
title = {Forest Fire Detection Using Convolutional Neural Networks},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {14},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {323-325},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1146},
doi = {https://doi.org/10.26438/ijcse/v7i14.323325}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i14.323325}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1146
TI - Forest Fire Detection Using Convolutional Neural Networks
T2 - International Journal of Computer Sciences and Engineering
AU - Shruthi G, Disha Bhat, Gagana H, Dimple M K, Kavitha
PY - 2019
DA - 2019/05/15
PB - IJCSE, Indore, INDIA
SP - 323-325
IS - 14
VL - 07
SN - 2347-2693
ER -

           

Abstract

there have been many technologies developed recently on embedded processing that have enabled the vision based systems to detect fire using convolutional neural networks (CNN). All such methods need large memory and more computational time. In this research paper we initiate more efficient fire detection strategy with high performance. Here we are considering computational complexity and exact model for the problem by comparing other computational expensive networks. By considering the nature of problem statement, we can increase the efficiency and accuracy of the model. The results on benchmark datasets of fire shows us the efficient work of the proposed system with validation for detection of fire under cctv maintenance compared to other art of methods.

Key-Words / Index Term

CNN, Fire detection, stride, filters, pooling, surveillance videos

References

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