Key points are not available for this paper at this time.
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.
Zhao et al. (Thu,) studied this question.