Explainable Convolutional Networks for Crater Detection and Lunar Landing Navigation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 34 references that are in the map. First posted on arXiv 24 Aug 2024.
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13 of 34 references.
References · 13
- 2017Attention Is All You Needcited by 59,932
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2018CBAM: Convolutional Block Attention Modulecited by 2,463
- 2015Spatial Transformer Networkscited by 1,423
- 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,186
- 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
- 2017DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networkscited by 173
- 2021Deep Learning-based Spacecraft Relative Navigation Methods: A Surveycited by 17
- 2021Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landingcited by 6
- 2020Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detectioncited by 5
- 2020Automated Crater Detection from Co-registered Optical Images, Elevation Maps and Slope Maps using Deep Learningcited by 5
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- 2016Crater Detection via Convolutional Neural Networkscited by 4
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