ESP32-Based Intelligent Fire Detection System Utilizing Machine Learning and Computer Vision for Enhanced Safety in Indian Micro and Small Enterprises

Abstract

Protection against fire hazards remains a critical concern for small to medium businesses with limited financial resources. This research introduces an intelligent fire detection system leveraging machine learning and real-time image processing to address conventional fire detection limitations. The proposed system integrates an ESP32 microcontroller with an Arduino-based camera module, creating an affordable solution for detecting smoke, flames and heat through video stream analysis. By implementing advanced algorithms, the system provides efficient and timely fire alarms while minimizing false positive rates and enhancing operational safety. The modular design enables seamless integration with existing security infrastructures, requiring minimal reconfiguration. Extensive testing under diverse conditions demonstrates the model's adaptability, achieving high accuracy and reliability. Specifically developed to address fire safety gaps in the Indian industrial context, the solution targets micro and small enterprises, promoting technological advancement, industrial safety and infrastructure development at a cost-effective price point. The research contributes to bridging critical safety technology gaps for resource-constrained businesses.