Next Article in Journal
An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities
Previous Article in Journal
Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers

Image-Based Automatic Watermeter Reading under Challenging Environments

School of Informatics, Xiamen University, Xiamen 361000, China
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 434;
Received: 30 November 2020 / Revised: 26 December 2020 / Accepted: 4 January 2021 / Published: 9 January 2021
(This article belongs to the Section Intelligent Sensors)
With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated. View Full-Text
Keywords: watermeter reading; automatic method; neural network; deep learning watermeter reading; automatic method; neural network; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Hong, Q.; Ding, Y.; Lin, J.; Wang, M.; Wei, Q.; Wang, X.; Zeng, M. Image-Based Automatic Watermeter Reading under Challenging Environments. Sensors 2021, 21, 434.

AMA Style

Hong Q, Ding Y, Lin J, Wang M, Wei Q, Wang X, Zeng M. Image-Based Automatic Watermeter Reading under Challenging Environments. Sensors. 2021; 21(2):434.

Chicago/Turabian Style

Hong, Qingqi, Yiwei Ding, Jinpeng Lin, Meihong Wang, Qingyang Wei, Xianwei Wang, and Ming Zeng. 2021. "Image-Based Automatic Watermeter Reading under Challenging Environments" Sensors 21, no. 2: 434.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop