Benchmarking YOLO Models for Automatic Reading in Smart Metering Systems: A Performance Comparison Analysis

In the smart grid ecosystem, the automated reading of water meters is a critical task that can be accomplished with deep learning algorithms instead of the traditional way that relies on the physical presence of employees or utility representatives. The YOLO (You Only Look Once) family of models has gained prominence among these approaches because of its excellent accuracy and quick processing time. In this study, we compare the performance of four YOLO models for automated reading in water metering systems (YOLOv5, YOLOv6, YOLOv7, and YOLOv8). We assess the model's precision, recall, and processing time using a custom dataset of water meter images. In terms of precision and processing time, our testing results demonstrate that YOLOv8 surpasses YOLOv7, YOLOv6, and YOLOv5 by scoring 0.889 in precision, making it a suitable alternative for automated reading in smart metering systems. This benchmarking study provides a valuable reference for researchers and practitioners in the field of smart grid and deep learning, and it can aid in the selection of the most appropriate model for this task.