Multi-sourced sensing and support vector machine classification for effective detection of fire hazard in early stage. (July 2022)

• Record Type: Journal Article
• Authors: Chen, Siyuan; Ren, Jinchang; Yan, Yijun; Sun, Meijun; Hu, Fuyuan; Zhao, Huimin
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Fire incident detection -- Sensor fusion -- Machine learning -- Alarm systems -- Fire safety; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108046
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Proposed a multi-sensor system for detection of indoor fire incidents at early stage. Machine learning using support vector machine for effective classification. Improved accuracy with reduced false alarms for fire incident classification and detection. Cost-effective for embedded implementation and satisfying industrial needs. Abstract: 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. Graphical abstract: Image, graphical abstract