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All Journal Indonesian Journal on Computing (Indo-JC)
Muhammad Salman Farhan
Telkom University
Computer Science & IT

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Video Based Fire Detection Method Using CNN and YOLO Version 4 Muhammad Salman Farhan; Febryanti Sthevanie; Kurniawan Nur Ramadhani
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.654

Abstract

Fire detection is one of the technological efforts to prevent fire incidents. This is very important because the damage caused by fires can be minimized by having a fire detector. There are two types of fire detection, namely traditional-based and computer vision-based. Traditional-based fire detection has many shortcomings, one of which requires a close fire distance for activation. Hence, computer vision-based fire detection is made to cover the shortcomings of traditional-based fire detection. Therefore, in this study, we propose a video-based fire detection using a Convolutional Neural Network (CNN) Deep Learning approach supported by You Only Look Once (YOLO) object detection model version four. This study uses a dataset of various fire scenarios in the form of images and videos. The fire detection built in this study has an accuracy of above 90% with an average detection speed of 34.17 Frame Per Second (FPS).
Co-Authors Febryanti Sthevanie Kurniawan Nur Ramadhani