A cost-effective automatic dial meter reader using a lightweight convolutional neural network

With the vigorous development of the Internet of Things technology, the government has gradually phased out the traditional meter and began the era of smart meters. However, the replacement of smart meters is expensive and the yield is too low, which has led to the slow deployment of smart meters. Our idea is to develop a low-cost alternative solution that uses an edge device with a camera to automatically identify traditional electric dial meters, and then uploads the identified value to cloud servers. In the past, there have been studies to automatically read dial meters through traditional image segmentation methods. However, because traditional electric meters are mostly set in an environment with high concealment, dim light, and dirt, it is difficult for traditional methods to obtain good identification results for unclear meter images. In this paper, we propose a cost-effective automatic dial meter reader with a lightweight convolutional neural network on edge devices. In order to easily deploy and improve the accuracy of dial meter recognition, the proposed meter reader has the ability to automatically adjust tilt meter images. Experimental results show that the proposed lightweight convolutional neural network achieves significant improvements in segmentation errors, false positives, and elapsed time compared with the relative approaches.