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Comparison of Computer Vision Approaches in Application to the Electricity and Gas Meter Reading

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Evaluation of Novel Approaches to Software Engineering (ENASE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1172))

Abstract

This chapter presents comparison of computer vision approaches in application to the meter reading process for the standard (non-smart) electricity and gas. In this work, we analyse four techniques, Google Cloud Vision, AWS Rekognition, Tesseract OCR, and Azure’s Computer Vision. Electricity and gas meter reading is a time consuming task, which is done manually in most cases. There are some approaches proposing use of smart meters that report their readings automatically. However, this solution is expensive and requires both replacement of the existing meters, even when they are functional and new, and extensive changes of the whole meter reading system dealing.

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Notes

  1. 1.

    https://cloud.google.com/vision.

  2. 2.

    https://aws.amazon.com/rekognition.

  3. 3.

    https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision.

  4. 4.

    https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision.

  5. 5.

    https://www.hyperledger.org.

  6. 6.

    https://www.docker.com.

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Acknowledgements

We would like to thank Shine Solutions Group Pty Ltd for sponsoring this project under the research grant RE-03615. We also would like to thank Energy Australia for collaboration in this project. We also would like to thank the experts from the Shine Solutions Group, especially Aaron Brown and Alan Young for numerous discussions as well as their valuable advice and feedback.

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Authors and Affiliations

  1. School of Science, RMIT University, Melbourne, Australia

    Maria Spichkova, Johan van Zyl, Siddharth Sachdev, Ashish Bhardwaj & Nirav Desai

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  1. Maria Spichkova
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  2. Johan van Zyl
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  3. Siddharth Sachdev
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  4. Ashish Bhardwaj
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  5. Nirav Desai
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Corresponding author

Correspondence to Maria Spichkova .

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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

    Ernesto Damiani

  2. Department of Computer Science, City University of London, London, UK

    George Spanoudakis

  3. Wroclaw University of Economics, Wroclaw, Poland

    Leszek A. Maciaszek

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Spichkova, M., van Zyl, J., Sachdev, S., Bhardwaj, A., Desai, N. (2020). Comparison of Computer Vision Approaches in Application to the Electricity and Gas Meter Reading. In: Damiani, E., Spanoudakis, G., Maciaszek, L. (eds) Evaluation of Novel Approaches to Software Engineering. ENASE 2019. Communications in Computer and Information Science, vol 1172. Springer, Cham. https://doi.org/10.1007/978-3-030-40223-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-40223-5_15

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