Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines
This work addresses the need for efficient meter reading in utilities, particularly for non-smart meters, but is incremental as it applies existing deep learning methods to a new dataset.
The paper tackled the problem of automatically reading dial meters from images, a common non-smart meter type, by introducing a public dataset and deep learning baselines, achieving 100% F1-score for dial detection and up to 93.6% recognition accuracy for dials.
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 Paraná (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).