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Article

Image-Based Automatic Watermeter Reading under Challenging Environments

1
School of Informatics, Xiamen University, Xiamen 361000, China
2
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; https://doi.org/10.3390/s21020434
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
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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. https://doi.org/10.3390/s21020434

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. https://doi.org/10.3390/s21020434

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. https://doi.org/10.3390/s21020434

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