LAUSR

Reliable fire detection is pivotal for safety in both domestic and industrial environments. Traditional detection methods often suffer from false alarms and environmental interference, thus proving inadequate in critical scenarios. In this study we propose a novel flame detector based on a voltage-tunable dual-band Ge-on-Si photodetector combining a wavelength sensitive detection with classification algorithms. We evaluated its performance against various interference sources, exploiting machine learning models such as Support Vector Machines (SVM) and Quadratic SVM, achieving up to 99.8% classification accuracy. We demonstrate the system can effectively distinguish flames from background illumination. In addition, the sensor is able to detect hot objects, making the device suitable for early hazard detection, as well. This combination of the unique optical property of the wavelength sensitive photodetector and powerful machine learning algorithms may represent a significant advancement in fire detection technology.