Machine Learning-Based On-Device Smoke and Flame Detection System Using Light Intensity of RGB Color Sensor
Year: 2025
Type: article
Abstract: 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.
Authors: Minsung Park, Bo-Hee Lee, Haiyoung Jung
Institution: Semyung University
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7128093473