Development of Real-Time Fire Detection Robotic System with Hybrid-Cascade Machine Learning Detection Structure

Abstract

Fire is a destructive hazard impacting residential, industrial, and forested environments. Once ignited, fire becomes difficult to control, and recovery efforts are often extensive. Therefore, early detection is critical for effective firefighting. This study presents a mobile robotic system designed for early fire detection, integrating a Raspberry Pi, RGB (red, green and blue), and night vision-NIR (near infrared reflectance) cameras. A four-stage hybrid-cascade machine learning model was developed by combining state-of-the-art (SotA) models separately trained on RGB and NIR images. The system accounts for both daytime and nighttime conditions, achieving F1 scores of 96.7% and 95.9%, respectively, on labeled fire/non-fire datasets. Unlike previous single-stage or two-stage vision pipelines, our work delivers a lightweight four-stage hybrid cascade that jointly fuses RGB and NIR imagery, integrates temporal consistency via ConvLSTM, and projects a robot-centric “safe-approach distance” in real time, establishing a novel edge-level solution for mobile robotic fire detection. Based on real-life test results, the robotic system with this new hybrid-cascade model could detect the fire source from a safe distance of 500 mm and with notably higher accuracy compared to structures with other models.