Image-based Automatic Dial Meter Reading in Unconstrained Scenarios

TL;DR

This paper introduces a novel image-based system for automatic dial meter reading in unconstrained environments, significantly reducing errors and improving recognition accuracy, with potential to replace manual readings in developing countries.

Contribution

It presents new approaches for ADMR, including a dataset and a combined YOLOv4 and regression method that outperforms previous techniques in accuracy.

Findings

Reduced MAE from 1,343 to 129

Achieved 98.90% meter recognition rate

Demonstrated effectiveness in unconstrained scenarios

Abstract

The replacement of analog meters with smart meters is costly, laborious, and far from complete in developing countries. The Energy Company of Parana (Copel) (Brazil) performs more than 4 million meter readings (almost entirely of non-smart devices) per month, and we estimate that 850 thousand of them are from dial meters. Therefore, an image-based automatic reading system can reduce human errors, create a proof of reading, and enable the customers to perform the reading themselves through a mobile application. We propose novel approaches for Automatic Dial Meter Reading (ADMR) and introduce a new dataset for ADMR in unconstrained scenarios, called UFPR-ADMR-v2. Our best-performing method combines YOLOv4 with a novel regression approach (AngReg), and explores several postprocessing techniques. Compared to previous works, it decreased the Mean Absolute Error (MAE) from 1,343 to 129 and achieved a meter recognition rate (MRR) of 98.90% -- with an error tolerance of 1 Kilowatt-hour (kWh).