Image Comparison Research of Smart Electricity Meter

Abstract

The standardization of the installation and operation and maintenance of the smart meter box is particularly important to ensure the reliable and safe operation and maintenance of the smart power system. The smart meter box is generally maintained by manual inspection, which requires experienced power professionals to judge the meter box. Status anomalies often lead to false negatives. With the development of fifth-generation mobile communication, machine learning and other technologies, operation and maintenance personnel can take on-site photos of the meter box through mobile devices and upload them to the cloud. The photos of regular inspections can be compared through the image analysis method based on machine learning to automatically determine whether the meter box is damaged. In this paper, target detection, image perspective transformation and adaptive local affine matching algorithm are combined. First, target detection is used to identify and locate the areas in the smart meter box that need to be compared, and the detected objects are cut into opposite subsections. Image, then transform the target to be determined through image perspective transformation, remove the area that does not need to be matched, and then use the adaptive local affine matching algorithm to calculate the similarity between each feature point according to the local features of the image. The accuracy of the image comparison process.