The Development of an AI-Based Fire Detection System for Hospital Buildings

Abstract

The study presented is aimed at implementing the fire detection technique by using the object detection approach at the hospitals in Kuwait. The objective is to overcome the shortcomings associated with the existing fire detection systems involving poor response time, poor accuracy, and frequency of maintenance required. The proposed approach involves a deep learning-based approach involving image processing to optimize the overall fire detection strategy, and improve the response time and accuracy. A parallel pooling approach has been proposed to enable the system to handle multiple data streams simultaneously. Such an approach offers improved efficiency in hospital settings where the detection of fire in a rapid manner is inevitable to avoid any disaster. By utilizing such a parallel pooling approach with deep learning object detection algorithms. The study offers a novel fire detection system. Following the Yolo architecture version 3 has been used for tracking the instances of fire and capturing the areas where any such instances exist.