Towards computer vision technologies: Semi-automated reading of automated utility meters
This work addresses the problem of automating utility meter reading for utility companies or service providers, but it is incremental as it builds on previous research and compares existing methods.
The study compared Tensorflow Object Detection and Anyline for semi-automated reading of utility meters, focusing on accuracy and inference time to identify the most suitable solution for this application.
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.