YOLOLens: A Deep Learning Model Based on Super-Resolution to Enhance the Crater Detection of the Planetary Surfaces

Abstract

The impact crater detection offers a great scientific contribution in analyzing the geological processes, morphologies and physical properties of the celestial bodies and plays a crucial role in potential future landing sites. The huge amount of craters requires automated detection algorithms, and considering the low spatial resolution provided by the satellite jointly with, the solar illuminance/incidence variety, these methods lack their performance in the recognition tasks. Furthermore, small craters are harder to recognize also by human experts and the need to have a sophisticated detection algorithm becomes mandatory. To address these problems, we propose a deep learning architecture refers as “YOLOLens5x”, for impact crater detection based on super-resolution in a unique end-to-end design. We introduce the entire workflow useful to link the Robbins Lunar catalogue with the tiles orthoprojected from the Lunar mosaic LROC mission in order to train our proposed model as a supervised paradigm and, the various optimization due to provide a clear dataset in the training step. We prove by experimental results a boost in terms of precision and recall than the other state-of-the-art crater detection models, reporting the lowest error estimated craters diameter using the same scale factor given by LROC WAC Camera. To simulate the camera satellite at the lowest spatial resolution, we carried out experiments at different scale factors (200 m/px, 400 m/px) by interpolating the source image of 100 m/px, bringing to light remarkable results across all metrics under consideration compared with the baseline used.