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Research on water meter reading recognition based on deep learning

At present, there are still many old-fashioned water meters in the society, and the water department needs to send staff to read the water meter after arriving at the scene with a handheld all-in-one machine. However, there are many problems in this manual meter reading method. First, a large number...

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Autores principales: Liang, Yue, Liao, Yiqi, Li, Shaobo, Wu, Wenjuan, Qiu, Taorong, Zhang, Weiping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329402/
https://www.ncbi.nlm.nih.gov/pubmed/35896797
http://dx.doi.org/10.1038/s41598-022-17255-3
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author Liang, Yue
Liao, Yiqi
Li, Shaobo
Wu, Wenjuan
Qiu, Taorong
Zhang, Weiping
author_facet Liang, Yue
Liao, Yiqi
Li, Shaobo
Wu, Wenjuan
Qiu, Taorong
Zhang, Weiping
author_sort Liang, Yue
collection PubMed
description At present, there are still many old-fashioned water meters in the society, and the water department needs to send staff to read the water meter after arriving at the scene with a handheld all-in-one machine. However, there are many problems in this manual meter reading method. First, a large number of meter reading work leads to low efficiency of the entire water department, consuming a lot of time and energy, and high labor costs; second, the water meters in natural scenes have problems such as serious dial contamination and other environmental factors that interfere with the meter reading staff, and the results of the meter reader cannot be verified later. In response to these problems, this paper studies a deep learning method for automatic detection and recognition of water meter readings. This paper first introduces the existing in-depth learning models, such as Faster R-CNN, SSD, and YOLOv3. Then two datasets are sorted out, one is the original water table picture dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Then two plans are proposed, one is the original water table image dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Finally, by comparing the three models from different angles, it is determined that YOLOv3 in the second solution has the best recognition effect, and the accuracy rate reaches 90.61%, which can greatly improve work efficiency, save labor costs, and assist auditors in reviewing the read water meter readings.
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spelling pubmed-93294022022-07-29 Research on water meter reading recognition based on deep learning Liang, Yue Liao, Yiqi Li, Shaobo Wu, Wenjuan Qiu, Taorong Zhang, Weiping Sci Rep Article At present, there are still many old-fashioned water meters in the society, and the water department needs to send staff to read the water meter after arriving at the scene with a handheld all-in-one machine. However, there are many problems in this manual meter reading method. First, a large number of meter reading work leads to low efficiency of the entire water department, consuming a lot of time and energy, and high labor costs; second, the water meters in natural scenes have problems such as serious dial contamination and other environmental factors that interfere with the meter reading staff, and the results of the meter reader cannot be verified later. In response to these problems, this paper studies a deep learning method for automatic detection and recognition of water meter readings. This paper first introduces the existing in-depth learning models, such as Faster R-CNN, SSD, and YOLOv3. Then two datasets are sorted out, one is the original water table picture dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Then two plans are proposed, one is the original water table image dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Finally, by comparing the three models from different angles, it is determined that YOLOv3 in the second solution has the best recognition effect, and the accuracy rate reaches 90.61%, which can greatly improve work efficiency, save labor costs, and assist auditors in reviewing the read water meter readings. Nature Publishing Group UK 2022-07-27 /pmc/articles/PMC9329402/ /pubmed/35896797 http://dx.doi.org/10.1038/s41598-022-17255-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liang, Yue
Liao, Yiqi
Li, Shaobo
Wu, Wenjuan
Qiu, Taorong
Zhang, Weiping
Research on water meter reading recognition based on deep learning
title Research on water meter reading recognition based on deep learning
title_full Research on water meter reading recognition based on deep learning
title_fullStr Research on water meter reading recognition based on deep learning
title_full_unstemmed Research on water meter reading recognition based on deep learning
title_short Research on water meter reading recognition based on deep learning
title_sort research on water meter reading recognition based on deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329402/
https://www.ncbi.nlm.nih.gov/pubmed/35896797
http://dx.doi.org/10.1038/s41598-022-17255-3
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