What is it about?
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., Mobile Net 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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Why is it important?
The evolution of a vision-based system assists detecting fire and smoke in real-time through visuals. During the fire accident, the fire spread quickly in the building with respect to environmental parameters, and the fire spreading inside the building creates a amount of huge smoke where the visibility becomes difficult for people stuck in the building. Thus, a real-time vision-based system assists the authorities in monitoring and providing evacuation paths and identifying the number of people stuck in the building, which eventually minimizes the loss of life. With the advantage of the vision-based system, we have proposed architecture and a system that is beneficial for implementing a real-time vision-based system by using Raspberry Pi and web application.
Perspectives
Fire detection and people estimation are very crucial during a fire accident. Early fire detection enables suggesting a safe exit path from the building for the people inside the building. Moreover, it is important to find the number of people stuck inside the building during a fire accident. Thus, in order to detect fires and to estimate the number of people in real time, we require a real-time visual capturing system. In this study, we have implemented a vision node based on Raspberry Pi with machine learning capabilities for detecting fire and estimating people count accurately in real time. Along with the vision node, we have designed a web interface that is capable of triggering all connected vision nodes by authorized signing in to the portal. Here, the vision node interfaces with the web portal through a local server hosted on Raspberry Pi.
Vaseem Akram Shaik
Read the Original
This page is a summary of: Realization of People Density and Smoke Flow in Buildings during Fire Accidents Using Raspberry and OpenCV, Sustainability, October 2021, MDPI AG,
DOI: 10.3390/su131911082.
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