Lunar ground segmentation using a modified U-net neural network

• Published in: Machine vision and applications Vol. 35; no. 3; p. 50
• Main Authors: Petrakis, Georgios, Partsinevelos, Panagiotis
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024; Springer Nature B.V
• Subjects: Autonomous navigation; Communications Engineering; Computer Science; Encoders-Decoders; Image Processing and Computer Vision; Image segmentation; Lightweight; Lunar surface; Machine learning; Moon; Networks; Neural networks; Pattern Recognition; Planetary rovers; Scene analysis; Semantic segmentation; Semantics; Synthetic data; Deep learning; Semantic segmentation; Planetary environments; Rover navigation
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-024-01533-3

Semantic segmentation plays a significant role in unstructured and planetary scene understanding, offering to a robotic system or a planetary rover valuable knowledge about its surroundings. Several studies investigate rover-based scene recognition planetary-like environments but there is a lack of a semantic segmentation architecture, focused on computing systems with low resources and tested on the lunar surface. In this study, a lightweight encoder-decoder neural network (NN) architecture is proposed for rover-based ground segmentation on the lunar surface. The proposed architecture is composed by a modified MobilenetV2 as encoder and a lightweight U-net decoder while the training and evaluation process were conducted using a publicly available synthetic dataset with lunar landscape images. The proposed model provides robust segmentation results, allowing the lunar scene understanding focused on rocks and boulders. It achieves similar accuracy, compared with original U-net and U-net-based architectures which are 110–140 times larger than the proposed architecture. This study, aims to contribute in lunar landscape segmentation utilizing deep learning techniques, while it proves a great potential in autonomous lunar navigation ensuring a safer and smoother navigation on the moon. To the best of our knowledge, this is the first study which propose a lightweight semantic segmentation architecture for the lunar surface, aiming to reinforce the autonomous rover navigation.