loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Maria Spichkova and Johan Van Zyl

Affiliation: School of Science, RMIT University, Melbourne, Victoria 3000, Australia

Keyword(s): Software Engineering, Computer Vision Techniques, Case Study.

Abstract: This paper presents a study on automated reading of utility meters using two computer vision techniques: an open-source solution Tensorflow Object Detection (Tensorflow) and a commercial solution Anyline. We aimed to identify the limitations and benefits of each solution applied to utility meters reading, especially focusing on aspects such as accuracy and inference time. Our goal was to determine the solution that is the most suitable for this particular application area, where there are several specific challenges.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.145.115.139

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Spichkova, M. and Van Zyl, J. (2020). Application of Computer Vision Technologies for Automated Utility Meters Reading. In Proceedings of the 15th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-443-5; ISSN 2184-2833, SciTePress, pages 521-528. DOI: 10.5220/0009892505210528

@conference{icsoft20,
author={Maria Spichkova. and Johan {Van Zyl}.},
title={Application of Computer Vision Technologies for Automated Utility Meters Reading},
booktitle={Proceedings of the 15th International Conference on Software Technologies - ICSOFT},
year={2020},
pages={521-528},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009892505210528},
isbn={978-989-758-443-5},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Software Technologies - ICSOFT
TI - Application of Computer Vision Technologies for Automated Utility Meters Reading
SN - 978-989-758-443-5
IS - 2184-2833
AU - Spichkova, M.
AU - Van Zyl, J.
PY - 2020
SP - 521
EP - 528
DO - 10.5220/0009892505210528
PB - SciTePress