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BIM and Computer Vision-Based Framework for Fire Emergency Evacuation Considering Local Safety Performance.

Hui Deng^1,2, Zhibin Ou¹, Genjie Zhang¹

¹School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This study introduces a new emergency evacuation navigation framework for public buildings. It uses real-time fire data to generate safe, local routes, improving occupant safety during fires.

Keywords:

BIM computer vision emergency evacuation fire hazard indoor positioning local safety performance

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Area of Science:

Engineering
Computer Science
Public Safety

Background:

Fire hazards in public buildings pose significant risks due to complex layouts and unpredictable fire spread.
Effective indoor navigation systems are crucial for safe and timely evacuation during emergencies.
Existing shortest route planning may not be optimal for dynamic fire scenarios.

Purpose of the Study:

To develop an emergency evacuation navigation framework prioritizing local safety performance.
To integrate Building Information Modeling (BIM) and computer vision for dynamic route generation.
To enhance occupant safety by providing real-time, situation-aware evacuation routes.

Main Methods:

Automatic construction of a geometry network model (GNM) from Building Information Modeling (BIM) data using Industry Foundation Classes (IFC).
Integration of computer vision for precise indoor positioning.
Application of a back propagation (BP) neural network to adjust routes based on available local egress time (ALET) and fire conditions.

Main Results:

A novel framework for emergency evacuation navigation was successfully developed and tested.
The generated rescue routes were demonstrated to be secure and reasonable for a campus building.
The system effectively utilizes real-time data for automatic rescue route generation.

Conclusions:

The proposed framework offers a passive, cost-effective, and convenient solution for automatic rescue route generation during fires.
This approach enhances local safety performance by dynamically adjusting routes to evolving fire situations.
The study provides a feasible method for real-time, image-based emergency navigation in buildings.