# Autonomous crater detection on asteroids using a fully-convolutional neural network

arXiv: https://arxiv.org/abs/2204.00477 · HTML: https://mlsanity.com/paper/2204.00477/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 65 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Deep Learning-based Spacecraft Relative Navigation Methods: A Survey](https://mlsanity.com/paper/2108.08876/) (2021)
- [Automated crater shape retrieval using weakly-supervised deep learning](https://mlsanity.com/paper/1906.08826/) (2019)
- [Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection](https://mlsanity.com/paper/2007.07702/) (2020)

## Built on by (most cited first, 2 of 2)

- [Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation](https://mlsanity.com/paper/2405.02642/) (2024)
- [Comparison of automated crater catalogs for Mars from Benedix et al. (2020) and Lee and Hogan (2021)](https://mlsanity.com/paper/2308.14650/) (2023)

## Abstract (arXiv)

This paper shows the application of autonomous Crater Detection using the U-Net, a Fully-Convolutional Neural Network, on Ceres. The U-Net is trained on optical images of the Moon Global Morphology Mosaic based on data collected by the LRO and manual crater catalogues. The Moon-trained network will be tested on Dawn optical images of Ceres: this task is accomplished by means of a Transfer Learning (TL) approach. The trained model has been fine-tuned using 100, 500 and 1000 additional images of Ceres. The test performance was measured on 350 never before seen images, reaching a testing accuracy of 96.24%, 96.95% and 97.19%, respectively. This means that despite the intrinsic differences between the Moon and Ceres, TL works with encouraging results. The output of the U-Net contains predicted craters: it will be post-processed applying global thresholding for image binarization and a template matching algorithm to extract craters positions and radii in the pixel space. Post-processed craters will be counted and compared to the ground truth data in order to compute image segmentation metrics: precision, recall and F1 score. These indices will be computed, and their effect will be discussed for tasks such as automated crater cataloguing and optical navigation.