A Review on Fire and Smoke Detection With Intelligent Control for Enhanced Safety Using Machine Learning (ML) and Internet of Things (IoT)

摘要

In today's world, combating fire accidents in high-risk environments like industrial facilities and large structures is a significant challenge. Deploying firefighters to such locations is not only perilous but also endangers their lives. To address such types of critical issues, this review paper suggests cutting-edge technologies, specifically, Machine Learning (ML) and Internet of Things (IoT) sensors, to develop autonomous fire-extinguishing robots. This suggested approach aims to enhance early fire detection and firefighting capabilities, prioritizing safety in hazardous environments. The system involves creating an intelligent bot using ML and IoT technologies. Outfitted with an array of sensors, including ultrasonic, lidar, gas detectors, and smoke detectors, the bot collects crucial data related to fire incidents. With features like cameras and microcontrollers, the bot allows seamless remote control. The ML capabilities embedded in the system empower the bot to detect fire and transmit relevant information for swift decision-making. By relying on sensor data, the bot aims to optimize control measures, minimizing risks for firefighters. This pioneering approach ensures enhanced safety measures and marks a significant stride toward a safer and more efficient future in firefighting operations. Through the convergence of ML, especially Convolutional Neural Networks and IoT, this solution presents a transformative paradigm for fire management in hazardous scenarios, promising a safer and more efficient future. This paper provides a thorough review of fire and smoke detection features, advantages, and innovative contributions to fire safety challenges using artificial intelligence. Additionally, we identified a research gap, noting that previous literature has primarily focused on traditional methods or fully autonomous solutions, with little attention given to hybrid approaches. In response to this gap, our review specifically explores and suggests the hybrid solution that integrates both traditional and autonomous firefighting techniques.