AI Fire Detection &amp; Notification System (AI 화재 탐지 및 알림서비스 시스템)

You-min Na (나유민); Dong-hwan Hyun (현동환); Dohyun Park (박도현); Se-hyun Hwang (황세현); Soo-Hong Lee (이수홍)

doi:10.9708/jksci.2020.25.12.063

AI Fire Detection & Notification System

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(12), pp.63-71
DOI : 10.9708/jksci.2020.25.12.063
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : November 27, 2020
Accepted : December 15, 2020
Published : December 31, 2020

You-min Na ¹, Dong-hwan Hyun ¹, Dohyun Park ¹, Se-hyun Hwang ¹, Soo-Hong Lee ¹

¹연세대학교

Accredited

ABSTRACT

In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.

KEYWORDS

Fire Detection, YOLOv3, EfficientDet, Notification, Real-time

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* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

AI Fire Detection & Notification System

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

AI Fire Detection & Notification System

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.