Applied Sciences (Feb 2023)

Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices

  • Rafaela Carvalho,
  • Jorge Melo,
  • Ricardo Graça,
  • Gonçalo Santos,
  • Maria João M. Vasconcelos

DOI
https://doi.org/10.3390/app13042315
Journal volume & issue
Vol. 13, no. 4
p. 2315

Abstract

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The ongoing reading process of digital meters is time-consuming and prone to errors, as operators capture images and manually update the system with the new readings. This work proposes to automate this operation through a deep learning-powered solution for universal controllers and flow meters that can be seamlessly incorporated into operators’ existing workflow. Firstly, the digital display area of the equipment is extracted with a screen detection module, and a perspective correction step is performed. Subsequently, the text regions are identified with a fine-tuned EAST text detector, and the important readings are selected through template matching based on the expected graphical structure. Finally, a fine-tuned convolutional recurrent neural network model recognizes the text and registers it. Evaluation experiments confirm the robustness and potential for workload reduction of the proposed system, which correctly extracts 55.47% and 63.70% of the values for reading in universal controllers, and 73.08% of the values from flow meters. Furthermore, this pipeline performs in real time in a low-end mobile device, with an average execution time in preview of under 250 ms for screen detection and on an acquired photo of 1500 ms for the entire pipeline.

Keywords