Correction and pointer reading recognition of circular pointer meter

For the meter images collected in an actual environment, there is the possibility of tilt and rotation. This paper presents a method to calibrate the circular pointer-type meter based on YOLOv5s network. The convolutional neural network framework is used to detect the scale value in the meter panel as the key point. The position information and value information of the detected scale value are used to fit the elliptic equation of the position of the scale value with the least square method for perspective correction and rotation correction of the meter, and the corrected meter image is used to obtain the meter pointer reading. This paper proposes the weighted angle method to read the meter reading. After multiple transformations, the accumulated error of the meter image is eliminated. Finally, comparing the pointer detection method of this paper with the traditional pointer detection method, the error of this detection method is smaller; comparing the meter reading results before and after correction, the meter reading error after correction is 50% less than before correction. Comparing the method in this paper with other mainstream methods, it proves the effectiveness of the our method.

Automated summary

This paper presents a method to calibrate circular pointer-type meters using the YOLOv5s network. The method utilizes a convolutional neural network framework for scale value detection and meter correction, resulting in accurate meter readings. By employing the weighted angle method, the accumulated error in meter images is eliminated, demonstrating the effectiveness of the proposed method.