Semantic Segmentation for Moon Rock Recognition Using U-Net with Pyramid-Pooling-Based SE Attention Blocks

Antoni Jaszcz, Dawid Połap

2024

Abstract

Analysis of data from the rover’s camera is an important element in the proper operation of unmanned vehicles. This is important because of the ability to move, avoid obstacles and even collect samples. In this paper, we propose a new U-Net architecture for rock/boulder recognition on the surface of the moon. For this purpose, architecture is composed of Squeeze and Excitation blocks extended with Pyramid Pooling and Convolution. As a result, such a network can pay attention to individual channels and give them weights based on global data. Moreover, the network analyzes contextual information in terms of local/global features in individual channels which allows for more accurate object segmentation. The proposed solution was tested on a publicly available database, achieving an accuracy of 97.23% and IoU of 0.7905.

Paper Citation

in Harvard Style

Jaszcz A. and Połap D. (2024). Semantic Segmentation for Moon Rock Recognition Using U-Net with Pyramid-Pooling-Based SE Attention Blocks. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 965-971. DOI: 10.5220/0012424600003636

in Bibtex Style

@conference{icaart24,
author={Antoni Jaszcz and Dawid Połap},
title={Semantic Segmentation for Moon Rock Recognition Using U-Net with Pyramid-Pooling-Based SE Attention Blocks},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={965-971},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012424600003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Semantic Segmentation for Moon Rock Recognition Using U-Net with Pyramid-Pooling-Based SE Attention Blocks
SN - 978-989-758-680-4
AU - Jaszcz A.
AU - Połap D.
PY - 2024
SP - 965
EP - 971
DO - 10.5220/0012424600003636
PB - SciTePress