Abstract: Line structured light 3D vision measurement is one of the main methods to obtain 3D data on the surface of an object, the traditional line structured light calibration results rely solely on the number of feature points on the extracted light streak, and in practice operations, it cannot guarantee the extraction of enough feature points to fit the plane. To address this problem, a new line structured light calibration method was proposed, the center of the camera was connected to any feature point in the center of the structured light stripe as a straight line, and there was no need to obtain the coordinates of all the points in the center line of the light stripe, but only to find the intersection of the light stripe with the checkerboard grid edge, which was used as the feature point of the light stripe.The number of such points was controlled, and a single linear regression was performed to obtain the equation of these characteristic points in pixel coordinates. Subsequently, this linear equation was combined with the equation of the chessboard plane to derive the corresponding feature point coordinates in the camera coordinate system. This process was repeated for three or more non-overlapping light stripes, enabling the fitting of a plane using multiple lines and enhancing calibration accuracy through a second-order regression. An experimental calibration was conducted using a line-structured light monocular camera setup in a laboratory environment. The results show an average distance of 0.052 mm between the characteristic points and the light plane, which falls within the acceptable calibration error range. This demonstrates that the proposed method effectively mitigates the issue of large calibration errors caused by a limited number of feature points.