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 - http://www.scopus.com/inward/record.url?scp=85129593123&partnerID=8YFLogxK
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 -