# Explainable Convolutional Networks for Crater Detection and Lunar Landing Navigation

arXiv: https://arxiv.org/abs/2408.13587 · HTML: https://mlsanity.com/paper/2408.13587/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [Deep Learning-based Spacecraft Relative Navigation Methods: A Survey](https://mlsanity.com/paper/2108.08876/) (2021)
- [Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing](https://mlsanity.com/paper/2102.10545/) (2021)
- [Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection](https://mlsanity.com/paper/2007.07702/) (2020)
- [Automated Crater Detection from Co-registered Optical Images, Elevation Maps and Slope Maps using Deep Learning](https://mlsanity.com/paper/2012.15281/) (2020)
- [Crater Detection via Convolutional Neural Networks](https://mlsanity.com/paper/1601.00978/) (2016)

## Built on by (most cited first, 1 of 1)

- [Adaptive Quantized Planetary Crater Detection System for Autonomous Space Exploration](https://mlsanity.com/paper/2508.18025/) (2025)

## 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.