Water Meter Reading Image Recognition Dataset

In the construction of smart cities today, water management faces the challenge of how to efficiently and accurately read water meters. Traditional manual methods are inefficient and prone to errors. Existing automated solutions often rely on simple image recognition algorithms, which are limited by varying environmental lighting and different types of meters, making accuracy hard to ensure. Therefore, developing a comprehensive dataset to help optimize water meter reading recognition technology is particularly important. This dataset aims to solve the technical issues of recognizing water meter readings in diverse environments, improving the precision and efficiency of automated water meter readings.Data collection is primarily conducted by installing high-resolution cameras on various types of water meters. The collection environment covers different lighting conditions and angles to ensure data diversity. In terms of quality control, data annotation undergoes multiple rounds of reviews to ensure accuracy, conducted by experienced water engineers. Preprocessing steps include image denoising, standardization, and data augmentation techniques such as rotation and scaling. Collected images are uniformly stored in JPG format and organized into groups based on water meter type and reading range.The core advantages of this dataset lie in its high annotation accuracy and consistency, with the reading annotation accuracy reaching up to 99% per image. Utilizing advanced image enhancement techniques like GAN (generative adversarial networks), it has improved the ability to parse low-quality images, increasing recognition rates under low-light conditions by more than 20%. Its application value lies in helping to enhance overall water management efficiency and reduce labor costs. Compared to similar datasets, it has richer scenarios and meter types, offering significant advantages in feature diversity and practicality. Additionally, this dataset is highly scalable and can be applied to other similar resource metering equipment recognition tasks.