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Build an anomaly detection ML model with Edge Impulse based on thermal images, with data sent over cellular to the cloud via the Notecard.

When I say “anomaly detection” you may imagine an overly complicated process, something exclusive to deep learning algorithms and indecipherable coding. In reality, the concept of uncovering anomalous behavior in a system is really just the act of identifying an unclassified or uncertain state.

In the two Machine Learning projects I’ve published here on Hackster (an ML speed trap and remote birding), I’ve created ML models with different data inputs, and asked them to generate inferences based on the known data provided. But what happens when I ask the model to create an inference about something it doesn’t know?

This, in essence, is what I tried to accomplish in this project.

I created an anomaly detection ML model with Edge Impulse that processes thermal images to detect unknown states of thermal readings and relays collected data to the cloud with the Blues Wireless Notecard.

While I built this around my home heating system, this type of solution could be used to monitor virtually any type of heat-producing equipment.

By using the “edge ML” capabilities provided by Edge Impulse, the cellular Notecard device-to-cloud data pump from Blues Wireless, a Raspberry Pi Zero 2, and an Ubidots dashboard, I was able to create a simple, low-power, thermal monitoring station with only a small bit of Python coding required”

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