Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

Abstract

Aiming at the problem that the number and decimal point of digital instruments in substations are prone to misdetection and missed detection, a method of digital meter readings in a substation based on connected domain analysis algorithm is proposed. This method uses Faster R-CNN (Faster Region Convolutional Neural Network) as a positioning network to localize the dial area, and after acquiring the partial image, it enhances the useful information of the digital area. YOLOv4 (You Only Look Once) convolutional neural network is used as the detector to detect the digital area. The purpose is to distinguish the numbers and obtain the digital area that may contain a decimal point or no decimal point at the tail. Combined with the connected domain analysis algorithm, the difference between the number of connected domain categories and the area ratio of the digital area is analyzed, and the judgment of the decimal point is realized. The method reduces the problem of mutual interference among categories when detecting YOLOv4. The experimental results show that the method improves the detection accuracy of the algorithm.

Keywords

• deep learning
• YOLOv4
• target detection
• connected domain
• digital meter readings