Smart meters enable remote and automatic electricity, water and gas
consumption reading and are being widely deployed in developed countries.
Nonetheless, there is still a huge number of non-smart meters in operation.
Image-based Automatic Meter Reading (AMR) focuses on dealing with this type of
meter readings. We estimate that the Energy Company of Paran\'a (Copel), in
Brazil, performs more than 850,000 readings of dial meters per month. Those
meters are the focus of this work. Our main contributions are: (i) a public
real-world dial meter dataset (shared upon request) called UFPR-ADMR; (ii) a
deep learning-based recognition baseline on the proposed dataset; and (iii) a
detailed error analysis of the main issues present in AMR for dial meters. To
the best of our knowledge, this is the first work to introduce deep learning
approaches to multi-dial meter reading, and perform experiments on
unconstrained images. We achieved a 100.0% F1-score on the dial detection stage
with both Faster R-CNN and YOLO, while the recognition rates reached 93.6% for
dials and 75.25% for meters using Faster R-CNN (ResNext-101).

本研究提出了一种基于深度学习的多表盘电表读取方法，这是首个尝试采用深度学习方法读取非智能表的研究，并在多个模型上进行了实验。通过本研究，我们提供了一个公共的、基于真实世界的电表数据集，同时还进行了详细的误差分析，对实际中存在的主要问题进行了评估。