Converting Utility Meters from Analogue to Smart based on Deep Learning Models
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Date
2020
Authors
Humberto J.Cabeza Barreto
Ilker Kurtulan
Suleyman Inci
Mert Nakıp
Cüneyt Güzeliş
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Open Access Color
Green Open Access
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No
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Average
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Top 10%
Abstract
In this paper we proposed a system that automatically interprets the data of the utility meters by analyzing the photo of an analogue meter. In addition it sends the meter data to the consumers and the providers. We based the system on Convolutional Neural Networks (CNN) where we compared the You Only Look Once (YOLO) and a LeNet as CNN models. We collected the data for the training of each CNN model from the demonstration set of the project. Our results show that the YOLO model is reliable and fast. The model has a 99% accuracy for the gas meter and 98% accuracy for the water meter. © 2020 Elsevier B.V. All rights reserved.
Description
Keywords
Analog Meters, Convolutional Neural Networks, Image Segmentation, Lenet, Machine Learning, Yolo, Convolutional Neural Networks, Intelligent Systems, Cnn Models, Learning Models, Deep Learning, Convolutional neural networks, Intelligent systems, CNN models, Learning models, Deep learning, YOLO, Analog Meters, Convolutional Neural Networks, Machine Learning, Image Segmentation, Lenet
Fields of Science
0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Citation
WoS Q
Scopus Q
OpenCitations Citation Count
3
Source
2020 Innovations in Intelligent Systems and Applications Conference ASYU 2020
Volume
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Start Page
1
End Page
4
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CrossRef : 1
Scopus : 4
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