RSA-PT: A Point Transformer-Based Semantic Segmentation Network for Uninterrupted Operation in a Distribution Network Scene
The digitization of uninterrupted operation in the distribution network is of great significance for improving people’s quality of life and promoting economic development. As an important means of achieving digitization, point cloud technology is crucial to the intelligent transformation of distribu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2370 |
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| _version_ | 1850180232643870720 |
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| author | Deyu Nie Linong Wang Shaocheng Wu Zhenyang Chen Yongwen Li Bin Song |
| author_facet | Deyu Nie Linong Wang Shaocheng Wu Zhenyang Chen Yongwen Li Bin Song |
| author_sort | Deyu Nie |
| collection | DOAJ |
| description | The digitization of uninterrupted operation in the distribution network is of great significance for improving people’s quality of life and promoting economic development. As an important means of achieving digitization, point cloud technology is crucial to the intelligent transformation of distribution network. To this end, the authors embedded the improved RSA (residual spatial attention) module and modified the loss function of network, proposing a deep learning network called RSA-PT for the semantic segmentation of a distribution network scene point cloud. According to the requirements of uninterrupted operation in the distribution network, the authors segmented the point cloud into the following ten classes: high-voltage line, low-voltage line, groundline, tower, ground, road, house, tree, obstacle, and car. Model and attention mechanism comparison experiments, as well as ablation studies, were conducted on the distribution network scene point cloud dataset. The experimental results showed that RSA-PT achieved <i>mIoU</i> (mean intersection over union), <i>mA</i> (mean accuracy), and <i>OA</i> (overall accuracy) indicators of 90.55%, 94.20%, and 97.20%, respectively. Furthermore, the <i>mIoU</i> of RSA-PT exceeded the baseline model by 6.63%. Our work could provide a technical foundation for the digital analysis of conditions for uninterrupted operation in distribution networks. |
| format | Article |
| id | doaj-art-0ac3d2e8e0694e34aef65096e6700932 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0ac3d2e8e0694e34aef65096e67009322025-08-20T02:18:15ZengMDPI AGSensors1424-82202025-04-01258237010.3390/s25082370RSA-PT: A Point Transformer-Based Semantic Segmentation Network for Uninterrupted Operation in a Distribution Network SceneDeyu Nie0Linong Wang1Shaocheng Wu2Zhenyang Chen3Yongwen Li4Bin Song5Engineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaEngineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaEngineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaEngineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaEngineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaEngineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaThe digitization of uninterrupted operation in the distribution network is of great significance for improving people’s quality of life and promoting economic development. As an important means of achieving digitization, point cloud technology is crucial to the intelligent transformation of distribution network. To this end, the authors embedded the improved RSA (residual spatial attention) module and modified the loss function of network, proposing a deep learning network called RSA-PT for the semantic segmentation of a distribution network scene point cloud. According to the requirements of uninterrupted operation in the distribution network, the authors segmented the point cloud into the following ten classes: high-voltage line, low-voltage line, groundline, tower, ground, road, house, tree, obstacle, and car. Model and attention mechanism comparison experiments, as well as ablation studies, were conducted on the distribution network scene point cloud dataset. The experimental results showed that RSA-PT achieved <i>mIoU</i> (mean intersection over union), <i>mA</i> (mean accuracy), and <i>OA</i> (overall accuracy) indicators of 90.55%, 94.20%, and 97.20%, respectively. Furthermore, the <i>mIoU</i> of RSA-PT exceeded the baseline model by 6.63%. Our work could provide a technical foundation for the digital analysis of conditions for uninterrupted operation in distribution networks.https://www.mdpi.com/1424-8220/25/8/2370point cloudsemantic segmentationdistribution networkuninterrupted operationpoint transformer |
| spellingShingle | Deyu Nie Linong Wang Shaocheng Wu Zhenyang Chen Yongwen Li Bin Song RSA-PT: A Point Transformer-Based Semantic Segmentation Network for Uninterrupted Operation in a Distribution Network Scene Sensors point cloud semantic segmentation distribution network uninterrupted operation point transformer |
| title | RSA-PT: A Point Transformer-Based Semantic Segmentation Network for Uninterrupted Operation in a Distribution Network Scene |
| title_full | RSA-PT: A Point Transformer-Based Semantic Segmentation Network for Uninterrupted Operation in a Distribution Network Scene |
| title_fullStr | RSA-PT: A Point Transformer-Based Semantic Segmentation Network for Uninterrupted Operation in a Distribution Network Scene |
| title_full_unstemmed | RSA-PT: A Point Transformer-Based Semantic Segmentation Network for Uninterrupted Operation in a Distribution Network Scene |
| title_short | RSA-PT: A Point Transformer-Based Semantic Segmentation Network for Uninterrupted Operation in a Distribution Network Scene |
| title_sort | rsa pt a point transformer based semantic segmentation network for uninterrupted operation in a distribution network scene |
| topic | point cloud semantic segmentation distribution network uninterrupted operation point transformer |
| url | https://www.mdpi.com/1424-8220/25/8/2370 |
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