Scholarlink

<p>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 evaluating. 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, recognition, and classification tasks across various challenge scenarios.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Comprehensive Dataset for Word-Wheel Water Meter Reading Under Challenging Conditions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月19日

查看全文
  • Shunyi Zhao,
  • Yibo Gao,
  • Fei Liu,
  • Yuxuan Zhang,
  • Jonghui Li

摘要

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 evaluating. 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, recognition, and classification tasks across various challenge scenarios.