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Actually, the scenes of OCS are complex, and the density of point cloud data obtained through Light Detection and Ranging (LiDAR) scanning is uneven due to the character difference of OCS components. However, due to the inconsistent component points, it is challenging to complete better semantic segmentation of the OCS point cloud with the existing deep learning methods. Therefore, this paper proposes a point cloud multi-scale feature fusion refinement structure neural network (PMFR-Net) for semantic segmentation of the OCS point cloud. The PMFR-Net includes a prediction module and a refinement module. The innovations of the prediction module include the double efficient channel attention module (DECA) and the serial hybrid domain attention (SHDA) structure. The point cloud refinement module (PCRM) is used as the refinement module of the network. DECA focuses on detail features; SHDA strengthens the connection of contextual semantic information; PCRM further refines the segmentation results of the prediction module. In addition, this paper created and released a new dataset of the OCS point cloud. Based on this dataset, the overall accuracy (OA), F1-score, and mean intersection over union (MIoU) of PMFR-Net reached 95.77%, 93.24%, and 87.62%, respectively. Compared with four state-of-the-art (SOTA) point cloud deep learning methods, the comparative experimental results showed that PMFR-Net achieved the highest accuracy and the shortest training time. At the same time, PMFR-Net segmentation performance on S3DIS public dataset is better than the other four SOTA segmentation methods. In addition, the effectiveness of DECA, SHDA structure, and PCRM was verified in the ablation experiment. The experimental results show that this network could be applied to practical applications.<\/jats:p>","DOI":"10.3390\/rs14122768","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Construction of a Semantic Segmentation Network for the Overhead Catenary System Point Cloud Based on Multi-Scale Feature Fusion"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1381-0558","authenticated-orcid":false,"given":"Tao","family":"Xu","sequence":"first","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianjun","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanwei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"},{"name":"Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4142-6254","authenticated-orcid":false,"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"China Railway Design Corporation, Tianjin 300308, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Xu","sequence":"additional","affiliation":[{"name":"National Petroleum and Natural Gas Pipeline Network Group Co., Ltd., Science and Technology Research Institute Branch, Langfang 065000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3317-6518","authenticated-orcid":false,"given":"Yanjun","family":"Wang","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China"},{"name":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.tranpol.2020.05.016","article-title":"The reconfiguration effect of China\u2019s high-speed railway on intercity connection \u2014A study based on media attention index","volume":"95","author":"Yang","year":"2020","journal-title":"Transp. 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