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

作者：Sai Zhao, Yibo Gao, Fei Liu, Yuxuan Zhang, J. Li · 发表于：Open MIND · 年份：2026 · DOI：10.5061/dryad.7d7wm3860 · 被引用次数：1 · 研究领域：Computer science、Artificial intelligence、Machine learning、Data mining、Remote sensing

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.