Remote Meter Reading based on Lightweight Edge Devices

Abstract

With the Industrial Internet of Things springs up, it is necessary to provide a remote and automatic meter reading solution for traditional enterprises and factories to avoid huge labor costs during production. However, traditional object detection solutions cannot be deployed on the current lightweight edge devices due to the high computational complexity of deep neural networks. In this paper, we propose a remote meter reading system, named EdgeMeter, which can provide a robust and real-time meter reading solution for lightweight edge devices. To ensure real-time and high-precision performance, we propose a lightweight feature point matching strategy to amortize the high latency of object detection, and we perform perspective correction to minimize the influence of meter orientations. To further improve the robustness of the system, we design a customized auxiliary device to eliminate the interference of complex outdoor environments. Real-world experiment results show that EdgeMeter achieves the meter reading error within 2.2° with a latency of less than 42 ms.