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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/7/1085 |
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| author | Xiaodong Yu Ta-Wen Kuan Shih-Pang Tseng Ying Chen Shuo Chen Jhing-Fa Wang Yuhang Gu Tuoli Chen |
| author_facet | Xiaodong Yu Ta-Wen Kuan Shih-Pang Tseng Ying Chen Shuo Chen Jhing-Fa Wang Yuhang Gu Tuoli Chen |
| author_sort | Xiaodong Yu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T01:05:35Z |
| format | Article |
| id | doaj.art-492c89ba9539441481c0c18779afb542 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T01:05:35Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-492c89ba9539441481c0c18779afb5422023-11-18T19:14:35ZengMDPI AGEntropy1099-43002023-07-01257108510.3390/e25071085EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise RoadsXiaodong Yu0Ta-Wen Kuan1Shih-Pang Tseng2Ying Chen3Shuo Chen4Jhing-Fa Wang5Yuhang Gu6Tuoli Chen7School of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, ChinaSchool of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, ChinaSchool of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, ChinaSchool of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, ChinaJiangsu Zero-Carbon Energy-Saving and Environmental Protection Technology, Yangzhou 225000, ChinaSchool of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, ChinaSchool of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, ChinaSchool of Information Science and Technology, Sanda University, No. 2727 Jinhai Road, Shanghai Pudong District, Shanghai 201209, ChinaRoad 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.https://www.mdpi.com/1099-4300/25/7/1085semantic segmentationmachine visionU-Net deep learningroad segmentationself-driving sweeping botresidual U-Net |
| spellingShingle | Xiaodong Yu Ta-Wen Kuan Shih-Pang Tseng Ying Chen Shuo Chen Jhing-Fa Wang Yuhang Gu Tuoli Chen EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads Entropy semantic segmentation machine vision U-Net deep learning road segmentation self-driving sweeping bot residual U-Net |
| 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 | semantic segmentation machine vision U-Net deep learning road segmentation self-driving sweeping bot residual U-Net |
| url | https://www.mdpi.com/1099-4300/25/7/1085 |
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