Fire and Smoke Detection and System Based on Multi-granularity Quantization Model

Fire detection has always been an important issue in the field of emergency. Traditional fire detection methods have many shortcomings, such as high hardware cost and limited application scenarios. This research constructs a detection process based on YOLOv10n models and creates corresponding multi-granularity fire datasets. The accuracy of fire and smoke detection on embedded devices reaches 66.58%. The multi-granularity detection model, based on quantization methods such as KL, ACIQ and EQ, is constructed to distinguish multiple fire and smoke types such as more smoke and less fire, less smoke and more fire, and average smoke and fire. Compared to a single quantization method, the average accuracy increases by 2.75%. The multi-granularity quantization model is verified on embedded devices, and a simulation system based on this model is implemented. The system distributed structure allows monitoring personnel to remotely interact with the model parameters, performing visual fire simulation to better adapt to various application scenarios.