EmberVision: Visual early Fire-detection sensor with Machine Learning on Microcontrollers

Traditional smoke detectors can delay fire detection in many scenarios when smoke must travel long distances, leading to damage to belongings and more lost lives as flames quickly become harder to control. This research presents EmberVision, a low-cost vision-based fire and smoke sensor that uses machine learning on microcontrollers to perform detection with minimal power consumption, no security concerns, and at a low cost. A unified fire and smoke dataset was created from 3 sources with 17,000 images, which was then used to train and optimize a scaled-down MobileNetV2-based binary classification CNN. The model achieved 94.4% accuracy pre-quantization, and after fine tuning with quantization aware training (QAT) and applying dynamic range quantization it maintained 90.5% accuracy with an 82% reduction of size to 616KB. A custom PCB module was then designed to run the model, consuming only 65mA during operation and with easy connectors to integrate it into a larger device. This module demonstrates the feasibility of an inexpensive visual early fire detection solution for the home that was traditionally unavailable but also acts as an blueprint of a way to deploy distributed machine learning on low-power devices to bring machine learning back from the cloud.

Ideaventions Academy for Mathematics and Science