北京交通大学学报2025，Vol.49Issue(3)：23-32,10.DOI:10.11860/j.issn.1673-0291.20240014
基于多尺度融合的道岔点云分割方法
Turnout point cloud segmentation method based on multi-scale fusion
摘要
Abstract
To address the limitations of current turnout track inspection methods,such as heavy reli-ance on manual labor,low detection efficiency,and the lack of depth information in 2D visual ap-proaches,this paper proposes a turnout point cloud segmentation method based on a multi-scale fusion strategy,named Point-Bidirectional Encoder Representations from Transformers-Turnout(Point-BERT-T).First,in the local point cloud encoding stage,spherical groupings with varying radii are employed to extract and fuse features from points within each sphere,generating a spatially hierarchi-cal hybrid feature representation.This multi-scale fused feature captures information at different spatial levels of the turnout,improving the efficiency and accuracy of railway infrastructure recognition and segmentation.It significantly enhances the capability of 3D point cloud recognition for turnouts and supports downstream tasks such as defect and deformation detection.Next,a random rotation-translation and non-uniform slicing strategy is introduced during data preprocessing to simulate real-world scanning variability,thereby improving the model's robustness under diverse data acquisition conditions.Finally,to validate the effectiveness of the proposed method,comparative experiments are conducted against existing approaches.The research results demonstrate that,compared to Point-BERT,the Point-BERT-T method improves the overall turnout point cloud segmentation perfor-mance by 1.9％.Furthermore,for the more challenging frog and wing rail components,the Intersec-tion over Union(IoU)increases by 4.7％and 5.6％,respectively,demonstrating the method's accu-racy and robustness in semantic segmentation of 3D turnout point cloud data.关键词
铁路巡检/道岔/深度学习/点云分割Key words
railway inspection/turnout/deep learning/point cloud segmentation分类
交通工程引用本文复制引用
宋奕霄,赵鑫欣,王胜春,严至成,李清勇..基于多尺度融合的道岔点云分割方法[J].北京交通大学学报,2025,49(3):23-32,10.基金项目
国家自然科学基金(K22A0300030) （K22A0300030）
中国国家铁路集团有限公司科技研究开发计划(K2022T006) （K2022T006）
中国铁道科学研究院集团有限公司基金资助项目(2022YJ256)National Natural Science Foundation of China(K22A0300030) （2022YJ256）
Science and Technology Research and Development Program of China National Railway(K2022T006) （K2022T006）
China Academy of Railway Sciences Foundation(2022YJ256) （2022YJ256）
