Abstract
The replacement of analog meters with smart meters is costly, laborious, and far from complete in developing countries. The Energy Company of Paraná (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 post-processing techniques. Compared to previous works, it decreased the Mean Absolute Error (MAE) from 1343 to 129 and achieved a meter recognition rate (MRR) of 98.9% — with an error tolerance of 1 Kilowatt-hour (kWh).
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