More models (or 1 larger model) would need to be trained to incorporate more target pieces. It only works to identify 1 piece at the moment.The field of view is limited, and we are only working with 240x240 resolution.It’s very slow, taking around 10 seconds to identify potential target pieces in a pile of bricks.Note that this project is a proof-of-concept with several major limitations: Here is a video showing the Lego brick finder in action: See this article to learn more about object detection and recognition. Identifying parts of an image to look for some object has many uses in autonomous vehicles, satellite and aerial image analysis, and X-ray image analysis. All of the code and image dataset used in this project can be found in this GitHub repository.Įven though this is a fun, lighthearted project showcasing the capabilities of machine learning on microcontrollers, it has many potential industrial applications. See this tutorial to learn how to set up and get started with the OpenMV camera. I attempt to address this problem using machine learning on microcontrollers (also known as TinyML). Even with their help, it could often take 15 minutes of digging through bins of Lego bricks to uncover that one elusive piece. Growing up, I often solicited help from my parents to assist in this arduous task. We will train a machine learning model to identify a particular piece with Edge Impulse and then deploy that model to the OpenMV, which will scan through captured images and highlight areas that match the piece on a connected LCD.įinding a particular Lego piece in a pile can be a time-consuming and frustrating task-a veritable needle-in-a-haystack. In this project, I will walk you through the steps needed to create your own LEGO® brick finder using the OpenMV H7 camera module.

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