Development of a CNN-Based Smoke/Fire Detection System for High-Risk Environments

Nwosu Ifeoma; Alagbu Ekene; Okeke Obinna; Ikpo Kingsely; Onwuamanam Chrysantus

Nwosu Ifeoma
Alagbu Ekene
Okeke Obinna
Ikpo Kingsely
Onwuamanam Chrysantus

Keywords: fire detection, CNN, deep learning

Abstract

This paper addresses the critical challenge of fire detection in high-risk environments, such as industrial facilities, warehouses, and densely populated areas. These locations face significant fire risks due to inherent processes and flammable materials. While traditional methods like smoke and heat detectors play a role, they have limitations like slow response times, false alarms, and ineffectiveness in large spaces. We propose a novel approach using a Convolutional Neural Network (CNN) for smoke/fire detection in these environments. Our CNN-based system continuously analyses video feeds, enabling faster fire detection compared to traditional methods and easily adapts its detection for scenes with challenging lighting conditions. This paper details the development, training process, and evaluation of the CNN system in simulated high-risk environments. The analysis of the system is performed in terms of accuracy, false alarm rate, and response time. The results demonstrate the potential of CNN technology for improving fire safety and early detection in critical locations, achieving an impressive accuracy of 94.14% and minimal loss of 0.14 during evaluation.

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