Hdf-RTFD: heterogeneous-data-fusion based real time fire detection system in AIoT environments

2021 ◽  
Author(s):  
Yen-Chiu Chen ◽  
Kun-Ming Yu ◽  
Huan-Po Hsu ◽  
You-Xiang Xu ◽  
Shang-Wei Tong ◽  
...  
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Detection System ◽  
Fire Detection ◽  
Heterogeneous Data ◽  
Fire Detection System

scholarly journals A Data Fusion-Based Fire Detection System

2018 ◽  
Vol E101.D (4) ◽  
pp. 977-984 ◽  
Author(s):  
Ying-Yao TING ◽  
Chi-Wei HSIAO ◽  
Huan-Sheng WANG
Keyword(s):  
Data Fusion ◽  
Detection System ◽  
Fire Detection ◽  
Fire Detection System

Real-Time Fire Detection System Based on Dynamic Time Warping of Multichannel Sensor Networks

2021 ◽  
pp. 103364
Author(s):  
Jaeseung Baek ◽  
Taha J. Alhindi ◽  
Young-Seon Jeong ◽  
Myong K. Jeong ◽  
Seongho Seo ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Dynamic Time Warping ◽  
Detection System ◽  
Fire Detection ◽  
Time Warping ◽  
Fire Detection System ◽  
Dynamic Time

scholarly journals Realization of People Density and Smoke Flow in Buildings during Fire Accidents Using Raspberry and OpenCV

2021 ◽  
Vol 13 (19) ◽  
pp. 11082
Author(s):  
Gajanand S. Birajdar ◽  
Mohammed Baz ◽  
Rajesh Singh ◽  
Mamoon Rashid ◽  
Anita Gehlot ◽  
...  
Keyword(s):  
Real Time ◽  
Web Application ◽  
Detection System ◽  
Human Life ◽  
Fire Detection ◽  
Smoke Detection ◽  
Urban Buildings ◽  
System A ◽  
Fire Detection System ◽  
Fire Accidents

Fire accidents in residential, commercial, and industrial environments are a major concern since they cause considerable infrastructure and human life damage. On other hand, the risk of fires is growing in conjunction with the growth of urban buildings. The existing techniques for detecting fire through smoke sensors are difficult in large regions. Furthermore, during fire accidents, the visibility of the evacuation path is occupied with smoke and, thus, causes challenges for people evacuating individuals from the building. To overcome this challenge, we have recommended a vision-based fire detection system. A vision-based fire detection system is implemented to identify fire events as well as to count the number people inside the building. In this study, deep neural network (DNN) models, i.e., MobileNet SSD and ResNet101, are embedded in the vision node along with the Kinect sensor in order to detect fire accidents and further count the number of people inside the building. A web application is developed and integrated with the vision node through a local server for visualizing the real-time events in the building related to the fire and people counting. Finally, a real-time experiment is performed to check the accuracy of the proposed system for smoke detection and people density.

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