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Finding the Signal from the Smoke: A Real-Time, Unattended Fire Prevention System Using 3D Convolutional Neural Networks

Published

Author(s)

Michael Ngai, Eugene Yujun Fu, Wai Cheong Tam, Grace Ngai, Amber Yang

Abstract

Cooking fires are dangerous. Every year, they are responsible for taking away more than 500 lives in the U.S. alone. Existing approaches using sensors usually require expensive retrofitting and are not feasible in real-life situations. This research presents Finding-Signals-from-Smoke (FiSS), a robust fire machine learning prediction model that aims to prevent cooking fires from starting using videos captured with a normal camera. FiSS is based on a 3-dimensional Convolutional Neural Network, which analyzes the video signals and models the complex relationships of the spatial-temporal features of smoke signals with fire ignition. It uses a segment-based video sampling and modeling framework that is able to generalize to a variety of kitchen/stove settings and achieve promising prediction performance. FiSS is trained and evaluated with video data from 30 full-scale kitchen fire experiments and can predict potential fire ignitions as early as 60 seconds before the moment of ignition. As a result, FiSS can be used in an early warning system to prevent fire ignitions and help to reduce casualties and injuries from cooking fires.
Citation
Journal of Student Research

Keywords

Cooktop Ignition, Machine learning, Kitchen Fire Prevention

Citation

Ngai, M. , Fu, E. , Tam, W. , Ngai, G. and Yang, A. (2022), Finding the Signal from the Smoke: A Real-Time, Unattended Fire Prevention System Using 3D Convolutional Neural Networks, Journal of Student Research, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935388 (Accessed May 20, 2024)

Issues

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Created August 31, 2022, Updated July 2, 2023