Aiming at the shortage of fog image data set with visibility labels，a visibility detection method based on simulation fog images is proposed. The depth map of clear outdoor images is constructed by unsupervised depth estimation model，and the details of the depth map are refined by using feature fusion. The transmission map of outdoor images under set visibility is obtained by using dark channel method to estimate atmospheric light value，and the simulation fog image dataset with different visibility labels is further obtained. Based on this，the improved ShuffleNet V2 network is adopted to train the visibility estimation model. A verification experiment is conducted on the visibility grade estimation of the dataset and the real foggy images. The experimental results show that: 1) The proposed method has good visibility estimation results for foggy images with visibility less than 500 meters;2) The detection accuracy is higher than 90% for foggy images with visibility less than 200 meters; and 3) The overall accuracy is 87.8%; which indicating that the method is feasible and can be applied to estimate the visibility level under fog conditions.