A Study of Novel Initial Fire Detection Algorithm Based on Deep Learning Method

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Electrical Engineering & Technology Pub Date : 2024-08-13 DOI:10.1007/s42835-024-02009-0
RaeHyun Yu, Kyungho Kim
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Abstract

A small ember, created by a chemical reaction between a substance and oxygen, can grow into a large fire as temperature, wind, and weather conditions change. A growing fire incident can have devastating consequences, including property loss, environmental damage, and loss of life, which is why early fire detection is so important. There are various detection devices such as smoke detectors, heat detectors, fire detectors, and gas detectors that can be used in the early stages of a fire. While early fire detection system developments incorporating IoT technology are emerging in various industries, Smoke alarms, the most common type of smoke detector in homes and offices, accounted for 96.6% of all malfunctions from 2021 to July of the previous year, totaling 249,445 incidents. The analysis of detector malfunctions showed that non-fire alarm factors such as food, cooking, and dust accounted for the largest share of 40.6%. This paper proposes an algorithm for early fire detection by incorporating a deep learning-based model to compensate for the problem of non-fire warning malfunctions, which is a shortcoming of existing detectors. Finally, for fire detection, a bounding box for the fire is specified using a smoke detector, a thermal imaging camera, and a webcam camera trained with the Yolov7 model. Then, we propose an algorithm to remove the bounding box of non-fire reports and malfunctions from the heating map using smoke detectors and thermal imaging cameras. After applying the algorithm proposed in this paper, only fires with heat sources are recognized, and all bounding boxes for non-fire reports are removed.

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基于深度学习方法的新型初期火灾探测算法研究
随着温度、风力和天气条件的变化,由物质和氧气之间的化学反应产生的小火苗可能会发展成大火。不断蔓延的火灾事故可能会造成破坏性后果,包括财产损失、环境破坏和生命损失,这就是早期火灾探测如此重要的原因。在火灾的早期阶段,可以使用烟雾探测器、热探测器、火灾探测器和气体探测器等各种探测设备。从 2021 年到上一年 7 月,烟雾报警器(家庭和办公室最常见的烟雾探测器)占所有故障的 96.6%,共发生 249445 起事故。对探测器故障的分析表明,食物、烹饪、灰尘等非火灾报警因素占比最大,达到 40.6%。本文通过结合基于深度学习的模型,提出了一种早期火灾探测算法,以弥补现有探测器的不足--非火灾预警故障问题。最后,为了进行火灾检测,我们使用烟雾探测器、热成像摄像机和使用 Yolov7 模型训练的网络摄像头来指定火灾的边界框。然后,我们提出了一种算法,利用烟雾探测器和热像仪从供热地图中删除非火灾报告和故障的边界框。应用本文提出的算法后,只有有热源的火灾才能被识别,所有非火灾报告的边界框都会被移除。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
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