Meter reading and mobile deployment based on deep learning

Yuxuan Chen; Zhenlu Li; Tianqi Yang; Haicheng Wang; Wei Huang

doi:10.1117/12.3005095

9 October 2023 Meter reading and mobile deployment based on deep learning

Yuxuan Chen, Zhenlu Li, Tianqi Yang, Haicheng Wang, Wei Huang

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127910W (2023) https://doi.org/10.1117/12.3005095
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

As a common measuring instrument, water meters are widely used in places such as water and power plants and households. The manual reading mode of old-fashioned water meters requires a large amount of manual and time costs. With the continuous breakthroughs in deep learning theory, it has become possible to use convolutional neural networks to automatically read old-fashioned meters. In order to improve the accuracy of meter reading and exclude other interference factors from the dial, this article proposes an attention-based DB network to make the model pay more attention to the meter display box, and uses a CRNN network for water meter reading, which effectively improves recognition accuracy. The model was tested and evaluated on a dataset of old-fashioned water and power plant meter dials, and the experimental results show good performance in the recognition accuracy of single and whole string characters. In addition, we deployed the model on Aiot to implement a high-precision and stable meter reading system.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuxuan Chen, Zhenlu Li, Tianqi Yang, Haicheng Wang, and Wei Huang "Meter reading and mobile deployment based on deep learning", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127910W (9 October 2023); https://doi.org/10.1117/12.3005095

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