Machine Learning-Based On-Device Smoke and Flame Detection System Using Light Intensity of RGB Color Sensor

This study proposes an edge-based real-time fire-detection system utilizing low-cost RGB sensors and TinyML technology. To overcome the high cost and maintenance limitations of existing detection methods in large indoor spaces, we propose an efficient approach that analyzes light-intensity patterns using affordable RGB sensors. Unlike simple threshold-based methods, our system applies machine learning to precisely distinguish between the complex optical patterns of smoke and flames, thus significantly enhancing detection accuracy. Designed with an on-device AI architecture, the system performs independent and rapid fire assessment on an edge device without requiring external network connectivity. Experiments conducted in a simulated fire environment confirmed the system’s feasibility and high accuracy. Spectral analysis and multisensor fusion shall be considered in future investigations to further minimize false alarms.