[Hardware] How to Build a Raspberry Pi Object Identification Machine

[Ale X Erfan]

In this tutorial, we will train our Raspberry Pi to identify other Raspberry Pis (or other objects of your choice) with Machine Learning (ML). Why is this important? An example of an industrial application for this type of ML is identifying defects in circuit boards. As circuit boards exit the assembly line, a machine can be trained to identify a defective circuit board for troubleshooting by a human.

We have discussed ML and Artificial Intelligence in previous articles, including facial recognition and face mask identification. In the facial recognition and face mask identification projects, all training images were stored locally on the Pi and the model training took a long time as it was also performed on the Pi. In this article, we’ll use a web platform called Edge Impulse to create and train our model to alleviate a few processing cycles from our Pi. Another advantage of Edge Impulse is the ease of uploading training images, which can be done from a smartphone (without an app).

We will use BalenaCloudOS instead of the standard Raspberry Pi OS since the folks at Balena have pre-built an API call to Edge Impulse. The previous facial recognition and face mask identification tutorials also required tedious command line package installs and Python code. This project eliminates all terminal commands and instead utilizes an intuitive GUI interface. 

What You’ll Need
Raspberry Pi 4, Raspberry Pi 400, or Raspberry Pi 3
8 GB (or larger) microSD card
Raspberry Pi Camera, HQ Camera, or USB webcam
Power Supply for your Raspberry Pi
Your smartphone for taking photos
Windows, Mac or Chromebook
Objects for classification