Using sensor fusion and tinyML to detect fires

Feb 13, 2023 ·

Sensor fusion and TinyML (machine learning on small embedded devices) can be used to detect fires before they become catastrophic. Sensor fusion combines data from multiple sensors such as thermal cameras and motion detectors in order to obtain a more accurate picture of the environment. The data is then fed into a machine learning model which can detect a fire before it starts.

TinyML uses algorithms designed to run on small hardware platforms such as Arduino boards, Raspberry Pi, and others. These platforms use limited resources, making them well suited for applications that require real-time response. By leveraging TinyML, sensor fusion can be used to detect fires before they become hazardous.

In addition, sensor fusion and TinyML can also be used to help identify the cause of a fire. For example, if a thermal camera detects an elevated temperature, a motion detector can be combined with other sensor data to determine what caused the fire. This type of analysis can allow firefighters to respond quickly and efficiently in order to prevent further damage.

Overall, sensor fusion and TinyML are powerful tools for detecting fires before they become catastrophic. They are particularly useful for identifying the source of the fire and alerting firefighters in time to prevent further damage. By leveraging the power of these two technologies, we can help ensure that fires are located and extinguished before they spread and cause significant harm.