To extract craters with a radius greater than 10 km more effectively from lunar digital elevation maps, pyramidal image segmentation based on the U-Net model is proposed, and the conversion relationship between the multilayer image pyramid and the geographic coordinates of the crater is established. The crater image pyramid method ensures the full coverage of the study area with a small number of images and that each crater exists in several images with different resolutions. The proposed method can effectively improve the detection performance of large-scale craters and solve the migration problem when stitching together craters from large-scale images. This method recovered 85.48% of the craters with a radius greater than 10 km in an artificially annotated dataset, found 1044 new craters, and extended the maximum radius of detected craters from 72 km in randomly cropped image segmentation to 200 km. It was estimated by visual interpretation that approximately 82.09% of these new craters are real. Also, the recall reaches 90.17% when the new real craters are added to the true craters.