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

Gajanand S. Birajdar, Mohammed Baz, Rajesh Singh, Mamoon Rashid, Anita Gehlot, Shaik Vaseem Akram, Sultan S. Alshamrani and Ahmed Saeed AlGhamdi
Additional contact information
Gajanand S. Birajdar: School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar 144411, India
Mohammed Baz: Department of Computer Engineering, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21994, Saudi Arabia
Rajesh Singh: School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar 144411, India
Mamoon Rashid: Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, India
Anita Gehlot: School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar 144411, India
Shaik Vaseem Akram: School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar 144411, India
Sultan S. Alshamrani: Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Ahmed Saeed AlGhamdi: Department of Computer Engineering, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21994, Saudi Arabia

Sustainability, 2021, vol. 13, issue 19, 1-19

Abstract: 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.

Keywords: fire detection; Raspberry Pi; deep neural network (DNN); MobileNet SSD; ResNet101; vision node (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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