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Automatic analog meter reading for plant inspection using a deep neural network

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Abstract

The high demand for automatic plant inspections by robots has been because a plant has many dangerous locations to cover during daily inspections. In this study, we propose automatically reading an analog meter using a Deep Neural Network (DNN), because reading an analog meter is included in the daily inspections. In particular, we artificially generate training data including shooting noise and apply these data for training the DNN. The learned DNN is robust against readable angles, meter contamination, and meter light reflection, and achieves a reading absolute error within 0.05. Additionally, we verify the effectiveness of the proposed method using cross-validation and accuracy comparison with conventional methods.

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Author information

Authors and Affiliations

  1. The University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu, Fukushima, 965-8580, Japan

    Yuki Funayama, Keita Nakamura, Taku Matsumoto & Yutaka Watanobe

  2. Makino Milling Machine Co., Ltd., 4007 Nakatsu, Aikawa-machi, Aiko-gun, Kanagawa, 243-0303, Japan

    Kenta Tohashi

  3. Aquacrew Inc., 42 Hon-machi, Inawashiro-machi, Yama-Gun, Fukushima, 969-3122, Japan

    Akira Sato

  4. Tier IV , Inc., Jacom Bldg., 1-12-10 Kitashinagawa, Shinagawa-ku, Tokyo, 140-0001, Japan

    Shigeki Kobayashi

Authors
  1. Yuki Funayama
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  2. Keita Nakamura
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  3. Kenta Tohashi
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  4. Taku Matsumoto
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  5. Akira Sato
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  6. Shigeki Kobayashi
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  7. Yutaka Watanobe
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Corresponding author

Correspondence to Yuki Funayama.

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This work was presented in part at the 25th International Symposium on Artificial Life and Robotics (Beppu, Oita, January 22–24, 2020)

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Funayama, Y., Nakamura, K., Tohashi, K. et al. Automatic analog meter reading for plant inspection using a deep neural network. Artif Life Robotics 26, 176–186 (2021). https://doi.org/10.1007/s10015-020-00662-y

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  • DOI: https://doi.org/10.1007/s10015-020-00662-y

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