Towards computer vision technologies: Semi-automated reading of automated utility meters
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Abstract (arXiv)
In this report we analysed a possibility of using computer vision techniques for automated reading of utility meters. In our study, we focused on two computer vision techniques: an open-source solution Tensorflow Object Detection (Tensorflow) and a commercial solution Anyline. This report extends our previous publication: We start with presentation of a structured analysis of related approaches. After that we provide a detailed comparison of two computer vision technologies, Tensorflow Object Detection (Tensorflow) and Anyline, applied to semi-automated reading of utility meters. In this paper, we discuss 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 65 references that are in the map. First posted on arXiv 24 Nov 2022.
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5 of 65 references.
References · 5
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2016Feature Pyramid Networks for Object Detectioncited by 5,036
- 2015SSD: Single Shot MultiBox Detectorcited by 3,130
- 2016Speed/accuracy trade-offs for modern convolutional object detectorscited by 198
- 2019Easy Mobile Meter Reading for Non-Smart Meters: Comparison of AWS Rekognition and Google Cloud Vision Approachescited by 2
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