Image Recognition and Reading of Single Pointer Meter Based on Deep Learning

With the development of smart grid, meter reading based on machine vision is gradually replacing manual meter reading. In substations or some industrial scenes, the environment of the meter is relatively harsh, and it is often affected by different environmental factors, such as light and pollution. The traditional meter reading shows significant performance degradation in noisy environments, and we propose an end-to-end algorithm for automatic identification and reading of single-pointer meters based on deep learning. This algorithm achieves the automatic identification and reading of single-pointer meters without requiring prior knowledge. The YOLOv5 network is employed for effective detection of the meter. Then, the perspective transformation method is used to correct the tilted dial plate. In order to solve the problem of low accuracy of pointer and tick mark extraction in low-quality images, we enhance the traditional U2NET network with a channel attention module (CAM) and spatial attention module (SAM). The CRAFT text detection algorithm and CRNN text recognition algorithm are utilized to determine the scale value of the dial plate. The experimental results show that the reading method proposed in this article has higher applicability than the existing methods, and the average reading error is less than 5% in the industrial field.