Electronic enthusiasts building projects that need to read analogue gauges, may be interested in a new article published to the official Arduino website this week providing more information about Nicla Vision. The system uses embedded machine learning and computer vision in order to autonomously read values and has been developed by the Zalmotek team.
Nicla Vision features a powerful processor with a 2MP color camera that supports TinyML and can easily be integrated into Edge Impulse. It also offers WiFi and Bluetooth Low Energy connectivity enabling you to send your data to the cloud without having to use another development board.
“Analog gauges are often used in industrial settings to measure various process variables such as pressure, temperature, and flow. In many cases, analog gauges are preferred over digital gauges because most analog gauges mounted on old machinery cannot be easily replaced or it would be too costly to do so. However, they have several disadvantages, such as requiring visual inspection by a human operator for reading them and the difficulty of integrating them into digital systems to automate tasks.”
“Computer Vision and Machine Learning can be used to overcome these disadvantages by retrofitting analog gauges with digital readouts. Computer Vision systems can automatically take readings from analog meters and displays, eliminating the need for manual reading and recording. In addition, this method provides real-time continuous monitoring of analog values, allowing for more accurate trending and analysis, reducing maintenance times, and enabling defining alerts to prevent failures.”
“In this tutorial we’ll show you how you can use Computer Vision and Machine Learning to read the boiler pressure gauge on a heating system. We’ll use the Arduino Nicla Vision camera to capture the training data and run the ML model, and the Edge Impulse platform to build, train and deploy an image classification model.”
Source: Read analogue gauges electronically with Nicla Vision