Multi-sourced sensing and support vector machine classification for effective detection of fire hazard in early stage

TY - JOUR

T1 - Multi-sourced sensing and support vector machine classification for effective detection of fire hazard in early stage

AU - Chen, Siyuan

AU - Ren, Jinchang

AU - Yan, Yijun

AU - Sun, Meijun

AU - Hu, Fuyuan

AU - Zhao, Huimin

N1 - Funding Information: This work is partially supported by the National Natural Science Foundation of China ( 61876125, 61876121, 62072122 ), Dazhi Scholarship of Guangdong Polytechnic Normal University, the Scientific and Technological Planning Projects of Guangdong Province (2021A0505030074) and the Scientific Research Abilities by Guangdong Key Construction Subject (2021ZDJS025). Copyright: © 2022 Elsevier Ltd. All rights reserved.

PY - 2022/7

Y1 - 2022/7

N2 - Accurate detection and early warning of fire hazard are crucial for reducing the associated damages. Due to the limitations of smoke-based detection mechanism, most commercial detectors fail to distinguish the smoke from dust and steam, leading to frequent false alarms and costly evacuation unnecessarily. To tackle this issue, we propose a fast and cost-effective indoor fire alarm system for real-time early fire detection under various scenarios, whilst significantly reducing the false alarms. Multimodal sensors are integrated to acquire the data of carbon monoxide, smoke, temperature and humidity, followed by effective data analysis and classification. For ease of embedded implementation, the support vector machine (SVM) is found to outperform the Random Forests (RF), K-means, and Artificial Neural Networks (ANN). On a public dataset and our own dataset, the proposed system performs promising, with the values of the precision, recall, and F1 of 99.8%, 99.6%, and 99.7%, respectively.

AB - Accurate detection and early warning of fire hazard are crucial for reducing the associated damages. Due to the limitations of smoke-based detection mechanism, most commercial detectors fail to distinguish the smoke from dust and steam, leading to frequent false alarms and costly evacuation unnecessarily. To tackle this issue, we propose a fast and cost-effective indoor fire alarm system for real-time early fire detection under various scenarios, whilst significantly reducing the false alarms. Multimodal sensors are integrated to acquire the data of carbon monoxide, smoke, temperature and humidity, followed by effective data analysis and classification. For ease of embedded implementation, the support vector machine (SVM) is found to outperform the Random Forests (RF), K-means, and Artificial Neural Networks (ANN). On a public dataset and our own dataset, the proposed system performs promising, with the values of the precision, recall, and F1 of 99.8%, 99.6%, and 99.7%, respectively.

KW - Alarm systems

KW - Fire incident detection

KW - Fire safety

KW - Machine learning

KW - Sensor fusion

UR - https://www.scopus.com/pages/publications/85129593123

U2 - 10.1016/j.compeleceng.2022.108046

DO - 10.1016/j.compeleceng.2022.108046

M3 - Article

AN - SCOPUS:85129593123

SN - 0045-7906

VL - 101

JO - Computers and Electrical Engineering

JF - Computers and Electrical Engineering

M1 - 108046

ER -