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Recognition of an Analog Display Instrument Based on Deep Learning

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1160))

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Abstract

With the development of remote automatic recording of utility meter readings, the problems of locating an analog display instrument against a complex background and recognition instruments with inconsistent scales are highlighted. This paper presents an automatic recognition method based on deep learning using the Faster-RCNN algorithm, which can locate the instrument position quickly and accurately in images with large amounts of noisy interference. By using the LeNet-5 convolutional neural network and the proportional relation between the pointer position and both ends of the scale, the number on the dial is recognized automatically. Experimental results showed that the Faster-RCNN algorithm can effectively detect the instrument position in images with large amounts of interference, thus laying an important foundation for subsequent pointer processing and reading recognition. Furthermore, the combination of number recognition and pointer location can make this method effective for different types of instruments with different ranges, which achieves better recognition than traditional approaches.

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Data Availability Statement

The original images [.jpg] data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgment

The authors thank the financial support for this work from Chinese National Natural Science Foundation (61672248, 61370105).

Author information

Authors and Affiliations

  1. Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong, China

    Zhihua Chen & Xiaoli Qiang

  2. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Kui Liu

Authors
  1. Zhihua Chen
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  2. Kui Liu
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  3. Xiaoli Qiang
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Corresponding author

Correspondence to Xiaoli Qiang .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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The authors declare that there is no conflict of interest regarding the publication of this paper.

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Chen, Z., Liu, K., Qiang, X. (2020). Recognition of an Analog Display Instrument Based on Deep Learning. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_44

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  • DOI: https://doi.org/10.1007/978-981-15-3415-7_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3414-0

  • Online ISBN: 978-981-15-3415-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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