Deployment framework for the Internet of water meters using computer vision on ARM platform
What is it about?
This article presents the conception of a new method developed mainly in Python to automate the reading process of water meters with an analog display using computer vision and machine learning. A camera captures the consumption value in the water meter, and the yielded image undergoes image processing until the digits are detected and isolated. Then the digits are passed into an SVM machine-learning model that carries out a high accuracy OCR. The software is executed over an ARM platform running Linux. The data resultant from the automated metering, such as the device identification number, event date and time in UTC, consumption value, volume and time variations, flow, and display image, are locally stored and transmitted to a cloud server through VPN in a Wi-Fi and cellular network connection, or by SMS, enabling a remote supervision. Thereby, the automatic metering method features a new way to perform predictive analysis and management of water and meters proactively and can be replicated for digital-display water meters, as well as extended to handle automatic metering on electricity and gas meters as well.