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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM23036

DETECTION OF FIRE COMBUSTION IN FORESTS USING DEEP LEARNING

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM23036

Volume:

7 |

6 |

Jun 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

J

Jellela.Vinila

KSSR

Kareddy. Sai Sathwik Reddy

M

Marepalli.Nikitha

MBKK

Mr. B. Kiran Kumar

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Abstract

Forest fires are a major environmental issue, creating economic and ecological damage while endangering human lives. There are typically about 100,000 wildfires in the United States every year. Over 9 million acres of land have been In this paper, the author uses CNN-convolutional neural networks to detect fire with the help of live video footage through anti-fire surveillance systems. The paper proposes YOLOv2 convolutional neural network is the best solution for detecting fire and smoke both indoor and outdoor environment. You only look once (YOLO) is a deep learning model for object detection, YOLOv2 is the next version which has been upgraded to rectify the setbacks of YOLO namely the inaccuracy to locate and mark the region of interest in the images and the lower recall rate compared to other region-oriented algorithms. Thus, increasing the efficiency of the architecture. They started with an input image of size 128x128x3. Top of Form

KEYWORDS

Fire detection Forest fires Deep learning Artificial neural networks

Cite This Article

Jellela.Vinila, Kareddy. Sai Sathwik Reddy, Marepalli.Nikitha, Mr. B. Kiran Kumar, "DETECTION OF FIRE COMBUSTION IN FORESTS USING DEEP LEARNING," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 6, Jun 2023. https://doi.org/10.55041/IJSREM23036

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Publication Date

7/5/2023

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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