Water Meter Reading for Smart Grid Monitoring

Abstract

Many tasks that require a large workforce are automated. In many areas of the world, the consumption of utilities, such as electricity, gas and water, is monitored by meters that need to be read by humans. The reading of such meters requires the presence of an employee or a representative of the utility provider. Automatic meter reading is crucial in the implementation of smart grids. For this reason, with the aim to boost the implementation of the smart grid paradigm, in this paper, we propose a method aimed to automatically read digits from a dial meter. In detail, the proposed method aims to localise the dial meter from an image, to detect the digits and to classify the digits. Deep learning is exploited, and, in particular, the YOLOv5s model is considered for the localisation of digits and for their recognition. An experimental real-world case study is presented to confirm the effectiveness of the proposed method for automatic digit localisation recognition from dial meters.