论文标题
基于图像的自动表盘读取在不受约束的情况下
Image-based Automatic Dial Meter Reading in Unconstrained Scenarios
论文作者
论文摘要
在发展中国家，用智能电表替换模拟仪的代价高昂，费力且远非完整。 Parana（Copel）（巴西）的能源公司每月执行超过400万米的读数（几乎完全是非智能设备），我们估计其中8.5万来自拨号表。因此，基于图像的自动阅读系统可以减少人体错误，创建阅读证明，并使客户能够通过移动应用程序执行阅读。我们提出了用于自动拨号计读数（ADR）的新颖方法，并在不受约束的场景中介绍了一个新的ADR数据集，称为UFPR-ADMR-V2。我们表现最佳的方法将Yolov4与新型回归方法（ANGREG）结合在一起，并探索了几种后处理技术。与以前的工作相比，它的平均绝对误差（MAE）从1,343降低到129，并达到仪表识别率（MRR）为98.90％ - 误差差为1千瓦时（kWh）。
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).
