Large-view, high-precision vision systems are becoming more and more in demand as industrial machine vision advances. Aiming at the issue of low accuracy caused by a large field of view, a joint calibration approach based on multiple low-pixel cameras is proposed. In a multi-camera setup, choose one camera to serve as the main camera. Then, determine the mapping matrix between the pixel coordinate systems of the other cameras and the main camera's pixel coordinate system such that the main camera's field of vision can be infinitely expanded. At the same time, to obtain the coordinates of the center pixel in the calibration plate image more precisely, the two-step calibration approach is employed to enhance the calibration accuracy. Extract the pixel coordinates of the center of the calibration plate for the first rough calibration, obtain the camera internal parameters and the calibration plate position and pose, and thus obtain the mapping correlation between the image plane and the plane Z=0 of the world coordinate system. After adjusting the perspective deviation, extract the center of the calibrated plate, use an inverse mapping transformation to return the associated center to its original location, and then calibrate the primary camera a second time using the converted center pixel coordinate position. Finally, the Levenberg-Marquardt algorithm is used for nonlinear optimization to achieve the global optimal solution. The experimental findings demonstrate that the re-projection error of the suggested calibration method is between 0.005 pixel-0.01 pixel.