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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...

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Autores principales: Yu, Xiaodong, Kuan, Ta-Wen, Tseng, Shih-Pang, Chen, Ying, Chen, Shuo, Wang, Jhing-Fa, Gu, Yuhang, Chen, Tuoli
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
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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.
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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
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