A comprehensive dataset for word-wheel water meter reading under challenging conditions

We present a comprehensive dataset designed for segmentation, recognition, and classification tasks related to word-wheel type water meter reading. This dataset encompasses a wide range of real-world scenarios, including clear, blurry, reflective, and obstructed images, captured under various environmental conditions. As a result, it provides a robust benchmark for model training and evaluation. It contains over 50,000 water meter images, annotated with segmentation masks, recognition labels, and multi-hot encoded classification labels. These annotations facilitate the training of models for segmentation, recognition, and multi-task classification, enabling them to address various challenges. Technical validation highlights the effectiveness and utility of the dataset in segmentation and recognition tasks across various challenge scenarios.

Methods
The images in this dataset were collected from the actual manual meter reading process in Hangzhou City, the second-largest city in the Yangtze River Delta region of China. The city's underground water infrastructure is complex, comprising many traditional water meters of varying ages. As such, the collected images of these traditional water meters are very representative of real-world conditions. The model trained with this dataset can be easily extended to other cities with similar infrastructure. In constructing this dataset, we took into account various real-world challenges based on the experiences of frontline meter reading workers. The water meter status in the dataset is highly varied, with different degrees of coverage and visual distortion resulting from factors such as shooting angles and obstructions.

The task of reading word-wheel type water meters from images involves two distinct steps: detection and recognition. The detection set construction process involves Image Screening, Size Standardization, Segmentation Label Annotation and Classification Label Annotation. Recognition set construction process involves Image Screening, Projection Transformation, Label Annotation.