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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM10822
Volume:
5 |
10 |
Oct 2021
Pages:
Publication Frequency:
Monthly (12 issues per year)
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.
YOLO achieved modern performance in categorization of images and other computer vision applications. Including them in fire detection devices will significantly improve detection accuracy, decreasing fire incidents and their effects on the natural world and society as a whole The main problem with founded fire detection technologies is their deployment in actual monitoring networks because to have significant data and computing inference needs. The total number of fires reported each year has risen because of human actions and a dry climate. Several ways of detection have been thoroughly examined. However, because of the significant memory and computing demands for inference, using CNN-algorithm fire detection techniques in actual observation networks is extremely difficult. Establishing a classification in the suggested plan.
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Publication Date
5/17/2023
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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