Cargando…
EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads
Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be protected and the sweeping efficiency of the SDSB pr...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378080/ https://www.ncbi.nlm.nih.gov/pubmed/37510032 http://dx.doi.org/10.3390/e25071085 |
_version_ | 1785079677547708416 |
---|---|
author | Yu, Xiaodong Kuan, Ta-Wen Tseng, Shih-Pang Chen, Ying Chen, Shuo Wang, Jhing-Fa Gu, Yuhang Chen, Tuoli |
author_facet | Yu, Xiaodong Kuan, Ta-Wen Tseng, Shih-Pang Chen, Ying Chen, Shuo Wang, Jhing-Fa Gu, Yuhang Chen, Tuoli |
author_sort | Yu, Xiaodong |
collection | PubMed |
description | Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be protected and the sweeping efficiency of the SDSB promoted. However, roads in the real world are generally exposed to intricate noise conditions as a result of changing weather and climate effects; these include sunshine spots, shadowing caused by trees or physical facilities, traffic obstacles and signs, and cracks or sealing signs resulting from long-term road usage, as well as different types of road materials, such as cement or asphalt; all of these factors greatly influence the effectiveness of road segmentation. In this work, we investigate the extension of Primordial U-Net by the proposed EnRDeA U-Net, which uses an input channel applying a Residual U-Net block as an encoder and an attention gate in the output channel as a decoder, to validate a dataset of intricate road noises. In addition, we carry out a detailed analysis of the nets’ features and segmentation performance to validate the intricate noises dataset on three U-Net extensions, i.e., the Primordial U-Net, Residual U-Net, and EnRDeA U-Net. Finally, the nets’ structures, parameters, training losses, performance indexes, etc., are presented and discussed in the experimental results. |
format | Online Article Text |
id | pubmed-10378080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103780802023-07-29 EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads Yu, Xiaodong Kuan, Ta-Wen Tseng, Shih-Pang Chen, Ying Chen, Shuo Wang, Jhing-Fa Gu, Yuhang Chen, Tuoli Entropy (Basel) Article Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be protected and the sweeping efficiency of the SDSB promoted. However, roads in the real world are generally exposed to intricate noise conditions as a result of changing weather and climate effects; these include sunshine spots, shadowing caused by trees or physical facilities, traffic obstacles and signs, and cracks or sealing signs resulting from long-term road usage, as well as different types of road materials, such as cement or asphalt; all of these factors greatly influence the effectiveness of road segmentation. In this work, we investigate the extension of Primordial U-Net by the proposed EnRDeA U-Net, which uses an input channel applying a Residual U-Net block as an encoder and an attention gate in the output channel as a decoder, to validate a dataset of intricate road noises. In addition, we carry out a detailed analysis of the nets’ features and segmentation performance to validate the intricate noises dataset on three U-Net extensions, i.e., the Primordial U-Net, Residual U-Net, and EnRDeA U-Net. Finally, the nets’ structures, parameters, training losses, performance indexes, etc., are presented and discussed in the experimental results. MDPI 2023-07-19 /pmc/articles/PMC10378080/ /pubmed/37510032 http://dx.doi.org/10.3390/e25071085 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yu, Xiaodong Kuan, Ta-Wen Tseng, Shih-Pang Chen, Ying Chen, Shuo Wang, Jhing-Fa Gu, Yuhang Chen, Tuoli EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads |
title | EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads |
title_full | EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads |
title_fullStr | EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads |
title_full_unstemmed | EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads |
title_short | EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads |
title_sort | enrdea u-net deep learning of semantic segmentation on intricate noise roads |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378080/ https://www.ncbi.nlm.nih.gov/pubmed/37510032 http://dx.doi.org/10.3390/e25071085 |
work_keys_str_mv | AT yuxiaodong enrdeaunetdeeplearningofsemanticsegmentationonintricatenoiseroads AT kuantawen enrdeaunetdeeplearningofsemanticsegmentationonintricatenoiseroads AT tsengshihpang enrdeaunetdeeplearningofsemanticsegmentationonintricatenoiseroads AT chenying enrdeaunetdeeplearningofsemanticsegmentationonintricatenoiseroads AT chenshuo enrdeaunetdeeplearningofsemanticsegmentationonintricatenoiseroads AT wangjhingfa enrdeaunetdeeplearningofsemanticsegmentationonintricatenoiseroads AT guyuhang enrdeaunetdeeplearningofsemanticsegmentationonintricatenoiseroads AT chentuoli enrdeaunetdeeplearningofsemanticsegmentationonintricatenoiseroads |