Read analog gauges electronically with Nicla Vision

Electronics enthusiasts building projects that need to read analog gauges may be interested in a new article posted this week on the official Arduino site providing more information on Nicla Vision. The system uses embedded machine learning and computer vision to read values ​​autonomously and was developed by the Zalmotek team.

Nicla Vision has a powerful processor with a 2MP color camera that supports TinyML and can easily be integrated with Edge Impulse. It also offers WiFi and Bluetooth Low Energy connectivity allowing you to send your data to the cloud without having to use another development board.

"Analog pressure 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 fitted to old machines cannot be easily replaced or it would be too expensive to do so, however they have several disadvantages such as the need for visual inspection by a human operator to read them and the difficulty of reading them. integrate into digital systems to automate tasks.”

“Computer vision and machine learning can be used to overcome these drawbacks by retrofitting analog gauges with digital readouts. Computer vision systems can automatically take readings from meters and sensors. 'analog displays, eliminating the need for manual reading and recording.In addition, this method provides continuous real-time monitoring of analog values, enabling more accurate trending and analysis, reducing maintenance time, and helping to set alerts to prevent outages."

"In this tutorial, we will show you how you can use computer vision and machine learning to read the boiler pressure gauge on a heating system. We will 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."

For more information, visit the official Zalmotek project page by following the link below.

Source: AB:Zalmotek

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