A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection

Automatic reading of pointer meters is of great significance for efficient measurement of industrial meters. However, existing algorithms are defective in the accuracy and robustness to illumination shooting angle when detecting various pointer meters. Hence, a novel algorithm for adaptive detection...

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Main Authors: Zhu Li, Yisha Zhou, Qinghua Sheng, Kunjian Chen, Jian Huang
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/20/5946
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spelling doaj-a6c8c118ecd04d6799a55015b8bdef5d2020-11-25T03:36:10ZengMDPI AGSensors1424-82202020-10-01205946594610.3390/s20205946A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text DetectionZhu Li0Yisha Zhou1Qinghua Sheng2Kunjian Chen3Jian Huang4School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310000, ChinaSchool of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310000, ChinaSchool of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310000, ChinaSchool of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310000, ChinaSchool of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310000, ChinaAutomatic reading of pointer meters is of great significance for efficient measurement of industrial meters. However, existing algorithms are defective in the accuracy and robustness to illumination shooting angle when detecting various pointer meters. Hence, a novel algorithm for adaptive detection of different pointer meters was presented. Above all, deep learning was introduced to detect and recognize scale value text in the meter dial. Then, the image was rectified and meter center was determined based on text coordinate. Next, the circular arc scale region was transformed into a linear scale region by polar transform, and the horizontal positions of pointer and scale line were obtained based on secondary search in the expanded graph. Finally, the distance method was used to read the scale region where the pointer is located. Test results showed that the algorithm proposed in this paper has higher accuracy and robustness in detecting different types of meters.https://www.mdpi.com/1424-8220/20/20/5946pointer meterdeep learningsecondary searchdistance method
collection DOAJ
language English
format Article
sources DOAJ
author Zhu Li
Yisha Zhou
Qinghua Sheng
Kunjian Chen
Jian Huang
spellingShingle Zhu Li
Yisha Zhou
Qinghua Sheng
Kunjian Chen
Jian Huang
A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection
Sensors
pointer meter
deep learning
secondary search
distance method
author_facet Zhu Li
Yisha Zhou
Qinghua Sheng
Kunjian Chen
Jian Huang
author_sort Zhu Li
title A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection
title_short A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection
title_full A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection
title_fullStr A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection
title_full_unstemmed A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection
title_sort high-robust automatic reading algorithm of pointer meters based on text detection
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-10-01
description Automatic reading of pointer meters is of great significance for efficient measurement of industrial meters. However, existing algorithms are defective in the accuracy and robustness to illumination shooting angle when detecting various pointer meters. Hence, a novel algorithm for adaptive detection of different pointer meters was presented. Above all, deep learning was introduced to detect and recognize scale value text in the meter dial. Then, the image was rectified and meter center was determined based on text coordinate. Next, the circular arc scale region was transformed into a linear scale region by polar transform, and the horizontal positions of pointer and scale line were obtained based on secondary search in the expanded graph. Finally, the distance method was used to read the scale region where the pointer is located. Test results showed that the algorithm proposed in this paper has higher accuracy and robustness in detecting different types of meters.
topic pointer meter
deep learning
secondary search
distance method
url https://www.mdpi.com/1424-8220/20/20/5946
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