EdgeMeter: An Edge Computing System and Algorithm for Intelligent Water Meter Recognition

Accurate and efficient water meter reading recognition system is essential for intelligent water resource management. However, existing systems face several challenges, including the high deployment costs of replacing old meters with smart ones, the limited device lifespan caused by local recognition on embed-ded devices, and the increased server workload associated with server-based processing. To address these issues, we propose an intelligent water meter recognition system based on a three-layer edge computing architecture. The IoT layer is responsible for data collection, utilizing an HC32F460 chip to capture automatically capture water meter images, compress the images, and transmit them to the edge layer. The edge layer is primarily composed of the YOLO-METER algorithm for water meter reading recognition. Based on YOLO11n, we have improved two modules by integrating FastC3k2 to enhance the extrac-tion of low-contrast features and MRFBlock to refine feature selection and im-prove the localization of reading regions on the water meter. The cloud layer periodically aggregates water meter readings, performs data analysis, and pro-vides users related information, enabling real-time monitoring and insights. Experiments show that YOLO-METER achieves a 2.4 higher mAP50 and 6 fewer parameters than YOLO11n, enhancing recognition accuracy while reduc-ing computational cost. This system facilitates efficient water usage monitoring, thereby improving operational efficiency and contributing to intelligent re-source management.