The paper proposes the use of Cascade Mask R-CNN for the detection of craters from monocular images. Crater detection is a challenging task being the images prone to changes in lighting and noise conditions. Besides, the crater appearance is strongly modified according to the region of interest, being the shadows strongly affected by the sun vector inclination. To tackle these issues, the paper exploits the generalizability of modern deep learning architectures to create a highly reliable crater detector. The dataset used for transfer learning the model comprises more than 800 real lunar monocular images obtained from the lunar reconnaissance orbiter (LRO) cameras. Results confirm the performance reached by the multi-stage object detection architecture both in equatorial and polar regions, its robustness, and the validity of this crater detection scheme for planetary navigation tasks.

A Deep Learning-based Crater Detector for Autonomous Vision-Based Spacecraft Navigation / Del Prete, R.; Saveriano, A.; Renga, A.. - (2022), pp. 231-236. (Intervento presentato al convegno 9th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2022 tenutosi a ita nel 2022) [10.1109/MetroAeroSpace54187.2022.9855951].

A Deep Learning-based Crater Detector for Autonomous Vision-Based Spacecraft Navigation

Del Prete R.;Renga A.
2022

Abstract

The paper proposes the use of Cascade Mask R-CNN for the detection of craters from monocular images. Crater detection is a challenging task being the images prone to changes in lighting and noise conditions. Besides, the crater appearance is strongly modified according to the region of interest, being the shadows strongly affected by the sun vector inclination. To tackle these issues, the paper exploits the generalizability of modern deep learning architectures to create a highly reliable crater detector. The dataset used for transfer learning the model comprises more than 800 real lunar monocular images obtained from the lunar reconnaissance orbiter (LRO) cameras. Results confirm the performance reached by the multi-stage object detection architecture both in equatorial and polar regions, its robustness, and the validity of this crater detection scheme for planetary navigation tasks.
2022
978-1-6654-1076-2
A Deep Learning-based Crater Detector for Autonomous Vision-Based Spacecraft Navigation / Del Prete, R.; Saveriano, A.; Renga, A.. - (2022), pp. 231-236. (Intervento presentato al convegno 9th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2022 tenutosi a ita nel 2022) [10.1109/MetroAeroSpace54187.2022.9855951].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/911410
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