Machine vision is crucial for detecting surface defects on cylindrical objects. While correcting the perspective distortion of images of cylindrical objects is feasible， shooting conditions pose challenges. Cylindrical objects are often observed in tilted positions. To address distortion caused by the perspective projection of inclined cylindrical object surfaces， a method for image correction based on cylindrical surface pose estimation was proposed. This method first extracted the side edges of cylindrical images and then estimated the pose relationship between the cylindrical surface and the camera by using the cylinder radius and the system's imaging parameters. An iterative algorithm with variable step size was employed to precisely calculate the cylindrical surface pose. Subsequently， a correspondence relationship between the subdivision mesh points of cylindrical surface and the original image pixel coordinates was established. The surface was unfolded into a plane， and a correspondence relationship between the unfolded subdivision mesh points and the corrected image pixel coordinates was established. This created a mapping relationship between the corrected image pixel coordinates and the original image pixel coordinates， allowing for resampling of the original image to obtain the corrected image. Experimental results demonstrate that the average distance error of cylindrical surface pose estimation is 0.3 mm， and the average angle error is 0.60°. The average distance standard deviation of adjacent corner points of a chessboard-patterned cylindrical object surface decreases from 12.2 pixels pre-correction to 0.8 pixels after correction. The corrected image effectively identifies text on the cylindrical surface， with a measurement error of defects on the cylindrical surface not exceeding 0.1 mm. The corrected image eliminates the inclined projection distortion and "near large， far small" perspective deformation of the cylindrical surface， validating the effectiveness of the proposed method.