To address the issue related to the recalibration of external parameters when a camera pose changes in the surround view, this paper proposes a method based on deep learning to correct the surround-view camera pose. First, a random pose-deviation value is added to the original image and the deviation image is converted into the input feature-extraction network in the bird's eye view. Second, a phased training strategy that uses different loss functions to learn the changes in the camera's key angle and position in the image is adopted. Finally, four directional features are aggregated and the six degree-of-freedom (DOF) pose-deviation values for each camera are returned. Experimental results show that the proposed method can estimate the camera pose deviation in real time, is better than similar calibration methods, and offers corrected surround-view images with an accuracy level suitable for practical applications.