Real-Time Fire Detection in Scenic Spot Using Convolutional Neural Network

The current fire-detection methods rely primarily on smoke and temperature detection, which are generally performed in the late stage of fire and thus cannot provide a timely reminder in the early stage of fire. The continuous development of artificial intelligence has enabled machine-vision fire de...

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Bibliographic Details
Published inJournal of Advanced Computational Intelligence and Intelligent Informatics Vol. 29; no. 2; pp. 432 - 437
Main Authors Yan, He, Merajuddin, Shaheem Sayed, Zhang, Miao
Format Journal Article
LanguageEnglish
Published Tokyo Fuji Technology Press Ltd 20.03.2025
富士技術出版株式会社
Fuji Technology Press Co. Ltd
Subjects
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
convolutional neural network (CNN)
Fire detection
fire detection in scenic spot
Informatics
Machine vision
Neural networks
Real time
Target detection
Tourism
Tourist attractions
YOLOv4
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Summary:The current fire-detection methods rely primarily on smoke and temperature detection, which are generally performed in the late stage of fire and thus cannot provide a timely reminder in the early stage of fire. The continuous development of artificial intelligence has enabled machine-vision fire detection. This study proposes a convolutional neural network target-detection algorithm, i.e., You Only Look Once version 4 (YOLOv4), to detect small targets. It offers outstanding characteristics and enables scenic-spot monitoring via the video extraction of real-time fire detection using a significant amount of fire data. The diverse fire scenes can provide accurate and timely detection in the early stage of fire, thus providing favorable early warning and alarm function.
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ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2025.p0432