A cost-effective automatic dial meter reader using a lightweight convolutional neural network

Cheng Hung Lin, Kuan Yi Kuo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the vigorous development of the Internet of Things technology, the government has gradually phased out the traditional meter and began the era of smart meters. However, the replacement of smart meters is expensive and the yield is too low, which has led to the slow deployment of smart meters. Our idea is to develop a low-cost alternative solution that uses an edge device with a camera to automatically identify traditional electric dial meters, and then uploads the identified value to cloud servers. In the past, there have been studies to automatically read dial meters through traditional image segmentation methods. However, because traditional electric meters are mostly set in an environment with high concealment, dim light, and dirt, it is difficult for traditional methods to obtain good identification results for unclear meter images. In this paper, we propose a cost-effective automatic dial meter reader with a lightweight convolutional neural network on edge devices. In order to easily deploy and improve the accuracy of dial meter recognition, the proposed meter reader has the ability to automatically adjust tilt meter images. Experimental results show that the proposed lightweight convolutional neural network achieves significant improvements in segmentation errors, false positives, and elapsed time compared with the relative approaches.

Original languageEnglish
Title of host publicationProceedings - 2020 13th International Conference on Human System Interaction, HSI 2020
PublisherIEEE Computer Society
Pages9-13
Number of pages5
ISBN (Electronic)9781728173924
DOIs
Publication statusPublished - 2020 Jun
Event13th International Conference on Human System Interaction, HSI 2020 - Tokyo, Japan
Duration: 2020 Jun 62020 Jun 8

Publication series

NameInternational Conference on Human System Interaction, HSI
Volume2020-June
ISSN (Print)2158-2246
ISSN (Electronic)2158-2254

Conference

Conference13th International Conference on Human System Interaction, HSI 2020
CountryJapan
CityTokyo
Period20/6/620/6/8

Keywords

  • Automatic meter recognition
  • Convolution neural networks
  • Electric dial meter
  • Internet of Things

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software

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