2024/08/24 by Jianing Song, Nabil Aouf, Song, Jianing +7 · 1 citation
Computer Science · Environmental Science · Physics and Astronomy · #Computational Physics and Python Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Methane Hydrates and Related Phenomena #Planetary Science and Exploration
paper · pdf · doi:10.48550/arxiv.2408.13587
openalex publication_date 2024/08/24 · openalex created_date 2024/09/21 · openalex updated_date 2026/07/28
The Lunar landing has drawn great interest in lunar exploration in recent years, and autonomous lunar landing navigation is fundamental to this task. AI is expected to play a critical role in autonomous and intelligent space missions, yet human experts question the reliability of AI solutions. Thus, the \glsxai for vision-based lunar landing is studied in this paper, aiming at providing transparent and understandable predictions for intelligent lunar landing. Attention-based Darknet53 is proposed as the feature extraction structure. For crater detection and navigation tasks, attention-based YOLOv3 and attention-Darknet53-LSTM are presented respectively. The experimental results show that the offered networks provide competitive performance on relative crater detection and pose estimation during the lunar landing. The explainability of the provided networks is achieved by introducing an attention mechanism into the network during model building. Moreover, the PCC is utilised to quantitively evaluate the explainability of the proposed networks, with the findings showing the functions of various convolutional layers in the network.