Perspective deformation correction for circular pointer meter based on prior indication structure feature

Abstract

The meter verification and industrial inspection robots often use automatic reading of pointer meters. Due to tilt and rotation of circular pointer meter, the perspective deformation may be induced to the captured image, so it will extremely affect the accuracy of automatic reading algorithm based on machine vision. Some existing deformation correction methods suffer from poor universality or low accuracy. The paper bases on prior indication structure feature of circular pointer meter and presents a new deformation correction method. First, the MobileNetV3-UNet network is used to detect the outer-endpoints of long scale lines in deformed meter as the key points. Second, with the help of prior information, the positions of mapping points in standard image are constructed. Third, according to the one-to-one mapping relationship of key points from deformed and standard images, a perspective transform matrix is calculated. Finally, the deformation and rotation correction are performed through inverse perspective transform. The experimental results show that the proposed method has better precision and robustness and works well under different conditions.