Analog instruments are everywhere and used to measure pressure, temperature, power levels, and much more. Due to the advent of digital sensors, many of these became quickly obsolete, leaving the remaining ones to require either conversions to a digital format or frequent human monitoring. However, the Zalmotek team has come up with a solution that incorporates embedded machine learning and computer vision in order to autonomously read these values.
Mounted inside of a custom enclosure, their project relies on an Arduino Pro Nicla Vision board, which takes periodic images for further processing and inference. They began by generating a series of synthetic gauge pictures that have the dial at various positions, and labeled them either low, normal, or high. This collection was then imported into the Edge Impulse Studio and used to train a machine learning model on the 96x96px samples due to the limited memory. Once created, the neural network could successfully determine the gauge’s state about 92% of the time.
The final step of this project involved deploying the firmware to the Nicla Vision and setting the image size to the aforementioned 96x96px size. By opting to use this technique of computer vision, frequent readings can be taken while also minimizing cost and power consumption.
More details on Zalmotek’s system can be found here on its Edge Impulse docs page.