Substation pointer meters detection and reading based on CNN

Abstract

Image processing methods based on feature matching are generally used for detecting and recognizing pointer meters in substation. Under the influence of environmental factors, such methods run into problems with low detection accuracy and reading success rate, when deployed in substation inspection robots. To improve the situation, a new method based on CNN (Convolutional Neural Network) for detecting and reading meters is proposed in this paper, through analyzing existing meter recognition process in robot’s vision subsystem. The new method detects and segments pointer meters using YOLOv3 (You Only Look Once) and U-Net separately, classifies scale values using AlexNet, and finally estimates readings though post-processing based on CNN models. The field experiment shows that, the proposed method has improved the reading success rate by 45% comparing to that of the conventional methods, while keeping the deviation within the permissible limits.