High-precision camera calibration via perspective distortion correction using a hybrid chessboard–circle target

Camera calibration is a critical prerequisite for precision optical measurement systems. Traditional methods predominantly rely on either chessboard or circular calibration targets, each exhibiting inherent limitations. Chessboard targets facilitate straightforward corner detection but suffer from relatively low localization accuracy. Circular targets enable high-precision center extraction but are susceptible to systematic errors induced by perspective distortion, which causes the projected ellipse center to deviate from the true physical circle center (eccentricity error). To overcome these limitations, this paper introduces a high-precision camera calibration method that leverages a novel, to our knowledge, hybrid calibration board embedding circular markers within a chessboard pattern. The proposed approach first detects readily detectable chessboard corners to compute a homography matrix, which rectifies local image regions parallel to the imaging plane, thereby transforming the elliptical marker projections into approximately circular shapes. The centers of these corrected markers are accurately extracted using image moments. Subsequently, an inverse perspective transformation projects the centers back to the original image plane to obtain their precise pixel coordinates, effectively mitigating the perspective-induced eccentricity error. Camera calibration is then performed using Zhang’s method with the refined coordinates. Extensive experiments demonstrate that the proposed method achieves an average reprojection error of 0.0365 pixels, representing a significant improvement of 51.13% compared to the traditional chessboard pattern and 35.05% compared to the circle pattern.