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.
Park et al. (Wed,) studied this question.