Lunar ground segmentation using a modified U-net neural network

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...

Full description

Saved in:
Bibliographic Details
Published in:Machine vision and applications 2024-05, Vol.35 (3), p.50, Article 50
Main Authors: Petrakis, Georgios, Partsinevelos, Panagiotis
Format: Article
Language:English
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
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-024-01533-3