Autonomous crater detection on asteroids using a fully-convolutional neural network
| Autor: | Latorre, F., Spiller, D., Curti, F. |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
aerospace engineering Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition FOS: Electrical engineering electronic engineering information engineering space systems optical navigation Electrical Engineering and Systems Science - Image and Video Processing asteroid crater detection artificial intelligence aerospace engineering space systems optical navigation |
| DOI: | 10.48550/arxiv.2204.00477 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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