Measurement error detection method of electric energy meter based on machine vision

Abstract

Due to the slow response and poor accuracy of traditional measurement error detection of the electric energy meter, the measurement error detection method of electric energy meter based on machine vision is studied. The minimum error method is used to segment the image threshold to form a binary image. The morphological refinement method is used to extract the image edge contour, combined with machine vision to refine the edge pixels, to achieve the measurement error detection of the instrument. The experimental results show that using the error detection method of machine vision, the detection results are consistent with the error detection results set by the system and the trend is the same. The accuracy also meets the requirements of relevant regulations, which improves the accuracy of electric energy meter measurement.