# Camera-Pose Robust Crater Detection from Chang'e 5

arXiv: https://arxiv.org/abs/2406.04569 · HTML: https://mlsanity.com/paper/2406.04569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Automated crater shape retrieval using weakly-supervised deep learning](https://mlsanity.com/paper/1906.08826/) (2019)
- [Lunar Crater Identification in Digital Images](https://mlsanity.com/paper/2009.01228/) (2020)
- [Automated crater detection with human level performance](https://mlsanity.com/paper/2010.12520/) (2020)

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

- [AI-Enabled Crater-Based Navigation for Lunar Mapping](https://mlsanity.com/paper/2509.20748/) (2025)

## Abstract (arXiv)

As space missions aim to explore increasingly hazardous terrain, accurate and timely position estimates are required to ensure safe navigation. Vision-based navigation achieves this goal through correlating impact craters visible through onboard imagery with a known database to estimate a craft's pose. However, existing literature has not sufficiently evaluated crater-detection algorithm (CDA) performance from imagery containing off-nadir view angles. In this work, we evaluate the performance of Mask R-CNN for crater detection, comparing models pretrained on simulated data containing off-nadir view angles and to pretraining on real-lunar images. We demonstrate pretraining on real-lunar images is superior despite the lack of images containing off-nadir view angles, achieving detection performance of 63.1 F1-score and ellipse-regression performance of 0.701 intersection over union. This work provides the first quantitative analysis of performance of CDAs on images containing off-nadir view angles. Towards the development of increasingly robust CDAs, we additionally provide the first annotated CDA dataset with off-nadir view angles from the Chang'e 5 Landing Camera.