DOI: 10.5061/dryad.7d7wm3860
title: A comprehensive dataset for word-wheel water meter reading under challenging conditions

description: 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.
description: 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 <em>Image Screening</em>, <em>Size
 Standardization</em>, <em>Segmentation Label
 Annotation</em> and <em>Classification Label
 Annotation</em>. Recognition set construction process involves
 <em>Image Screening</em>, <em>Projection
 Transformation</em>, <em>Label
 Annotation</em>.
description: # Word-Wheel Water Meter Dataset
 [https://doi.org/10.5061/dryad.7d7wm3860](https://doi.org/10.5061/dryad.7d7wm3860) ## Description of the data and file structure ### Files and variables #### File: Word-Wheel_Water_Meter_Dataset.zip **Description:** After downloading and unzipping the dataset, the root directory contains two primary subdirectories: one for detection and another for recognition. The detection directory is intended for training the model to detect and segment the reading area in water meter images, containing train set and test set. The directory structure of the train and test set is the same, and both include the following three files: 1. *det_img*: This subdirectory contains images to be segmented. The train set contains 32,714 images, which are named sequentially from *train0.png* to *train32713.png*, while test set contains 13,398 images named from *test0.png* to *test13397.png*. All images are in PNG format and have a resolution of 512×512 pixels. 2. *seg_label*: This subdirectory contains the segmentation labels. Each label file has the same name as its corresponding image in the *det_img* directory. The labels are also in PNG format and are the same size as the images. 3. *class_label.csv*: This CSV file contains scenario classification labels for each image. The first column contains the file name corresponding to image in the *det_img* directory as index. Columns two through seven contain multi-hot encoded binary strings representing the scenarios of this image. The recognition directory is used to train the digit recognition model. The images are categorized into two distinct subdirectories, each corresponding to either 5-digit or 6-digit readings. Within these subdirectories, there exists a training set and a test set, both of which encompass the following files: 1. *rec_img*: This subdirectory contains local images of water meter readings. The 5-digit dataset contains 11,867 training images (*train0.jpg* to *train11866.jpg*) and 2,000 test images (*test0.jpg* to *test1999.jpg*). The 6-digit dataset contains 2,555 training images (*train0.jpg* to *train2554.jpg*) and 400 test images (*test0.jpg* to *test399.jpg*). All images are in JPG format with a resolution of 60×200 pixels. 2. *rec_label.csv*: This CSV file includes the labels used during the recognition step. The first column contains the file name corresponding to image in the *rec_img* directory. The second column contains the 5-digit or 6-digit recognition label of this image. Columns three through five contain classification labels of this image. #### File: datacontent.png This image shows representative examples for every class in the dataset. ## Code/software The code used in this paper is available at [https://github.com/100000000Bo/water_meter_dataset_technical_validation_python_project.git](https://github.com/100000000Bo/water_meter_dataset_technical_validation_python_project.git). We provide code for detection and recognition tasks, including models for segmentation using UNet, PSPNet, DeepLabV3+, SegFormer, and models for recognition using CNNs, VGG16, ResNet, and DenseNet, along with training and testing scripts. The Python environment required is included in the *requirements.txt* file. CSV label testing codes *read_csv.py* are also provided in classification project to select specific CSV labels from the dataset for model evaluation.