Comparison of Deep Neural Network Learning Algorithms for Mars Terrain Image Segmentation
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Affiliation:
1. Gdańsk University of Technology, Poland
Publisher
University of Gdańsk
Reference8 articles.
1. 1. https://mars.nasa.gov/mars2020/spacecraft/rover/communications, Accessed 10.04.2024
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3. 3. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), ser. LNCS, vol. 9351. Springer, 2015, pp. 234-241, (available on arXiv:1505.04597 [cs.CV]).
4. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs;Chen;IEEE Transactions on Pattern Analysis and Machine Intelligence,2017
5. 5. Swan, R.M., Atha, D., Leopold, H.A., Gildner, M., Oij, S., Chiu, C., Ono, M.: AI4MARS: A dataset for terrain-aware autonomous driving on Mars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19821991 (2021)
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