An Intelligent Instrument Reader: Using Computer Vision and Machine Learning to Automate Meter Reading

被引：6

作者：

Sowah, Robert A. ^{[1
]}

Ofoli, Abdul R. ^{[2
]}

Mensah-Ananoo, Eugene ^{[1
]}

Mills, Godfrey A. ^{[1
]}

Koumadi, Koudjo M. M. ^{[1
]}

机构：

[1] Univ Ghana, Legon 00233, Accra, Ghana

[2] Univ Tennessee, Chattanooga, TN 37403 USA

来源：

IEEE INDUSTRY APPLICATIONS MAGAZINE | 2021年 / 27卷 / 04期

关键词：

Meters; Training; Feature extraction; Face recognition; Cameras; Shape; Machine learning algorithms;

D O I：

10.1109/MIAS.2021.3063082

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A novel algorithm using computer vision and machine learning techniques has been developed in this research and applied to automate the reading of analog meters. This approach does not rely on any prior information about the meter being read or any human intervention during the process. High-level features of the meter, including the graduation values and angles, are extracted using a cascade of image contour filters with a series of digit classifiers. The features are refined and used to train regression models that return the reading of the analog meter automatically.

引用

页码：45 / 56

页数：12

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