3DGraphSeg: A Unified Graph Representation- Based Point Cloud Segmentation Framework for Full-Range High-Speed Railway Environments

• Published in: IEEE transactions on industrial informatics Vol. 19; no. 12; pp. 11430 - 11443
• Main Authors: Geng, Yixuan, Wang, Zhipeng, Jia, Limin, Qin, Yong, Chai, Yuanyuan, Liu, Keyan, Tong, Lei
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.12.2023
• Subjects: Algorithms; Artificial neural networks; Back propagation; Datasets; Digital twins; Embedding; Entropy (Information theory); Graph representations; Graphical representations; High speed rail; Image segmentation; Railway engineering; Semantic segmentation; Topology
• DOI: 10.1109/TII.2023.3246492
• ISSN: 1551-3203

Point cloud semantic segmentation (PCSS) is crucial for digital twins of high-speed railways. By now, the concerned subjects are confined within the interior infrastructures of railways. However, the surrounding environments are also important for the safe operation. Concerning this issue, a full-range high-speed railway scanning scheme based on unmanned-aerial-vehicle-borne LiDAR is utilized. However, the massive data volume and data distribution imbalance pose great challenges for PCSS. To address these issues, a novel PCSS framework called 3DGraphSeg is proposed in this article. To cope with the massive data volume, a structural representation algorithm named local embedding super-point graph is proposed to represent the vast point cloud into a concise graph while retain the data's inherent topology structure by local spatial embedding. Then, the gated integration graph convolutional network (GIGCN) is proposed to contextual segment the graph. In the GIGCN, to prevent the gradients from vanishing or exploding, the hidden states of gated recurrent units in every layer are integrated using a new layer named gated hidden states integration (GHSI). GHSI strengthens the back propagation by giving the loss function direct access to each layer and absorbs the features of different layers comprehensively, which enables the network to produce a smoother decision boundary and prevents the overfitting problem. Besides, to enhance its robustness to data imbalance, we propose a loss function: adaptive weighted cross entropy. Finally, five experiments are designed for verification. The proposed framework has excelled in different datasets and outperforms state-of-the-art approaches on the SemanticRail dataset.