  Impact craters are formed as a result of continuous impacts on the surface of
planetary bodies. This paper proposes a novel way of simultaneously utilizing
optical images, digital elevation maps (DEMs), and slope maps for automatic
crater detection on the lunar surface. Mask R-CNN, tuned for the crater
detection task, is utilized in this paper. Two catalogs, namely, Head-LROC and
Robbins, are used for the performance evaluation. Exhaustive analysis of the
detection results on the lunar surface has been performed with respect to both
Head-LROC and Robbins catalog. With the Head-LROC catalog, which has relatively
strict crater markings and larger possibility of missing craters, recall value
of 94.28\% has been obtained as compared to 88.03\% for the baseline method.
However, with respect to a manually marked exhaustive crater catalog based on
relatively liberal marking, significant precision and recall values are
obtained for different crater size ranges. The generalization capability of the
proposed method in terms of crater detection on a different terrain with
different input data type is also evaluated. We show that the proposed model
trained on the lunar surface with optical images, DEMs and corresponding slope
maps can be used to detect craters on the Martian surface even with entirely
different input data type, such as thermal IR images from the Martian surface.
