Enhanced Fire Detection Using Deep Learning and Heat Signatures

Abstract

This study presents an innovative fire alarm system that integrates deep learning with thermal camera technology to tackle the urgent problem of slow fire detection, which often leads to considerable harm to individuals and extensive property damage. The system excels in identifying heat signatures prior to the full development of a fire, enabling timely notifications and thus mitigating potential damages and risks associated with fire incidents. Compared to traditional smoke detectors, this system operates at a considerably faster pace and offers a more flexible installation process by leveraging thermal cameras, which eliminates the need for ceiling-mounted detectors. Experimental evaluations demonstrate the efficacy of the proposed system, achieving up to 97% accuracy in fire detection. Simulations of various fire scenarios, ranging from initial heat detection to severe fire conditions, were used to train the system using a deep learning platform and a model of you only look once version 4 tiny (YOLOv4-Tiny). The results underscore the system's capability to detect early heat buildup swiftly, facilitating prompt alerts and enhancing overall fire safety.