MGLD-TLNet: Multigeometric and Long-Distance Representation Network for Transmission Line Inspection
针对输电线路巡检中点云稀疏、类别不平衡以及导体与塔架结构相关性强的问题，提出一种融合多几何与长距离空间关系的三维感知网络，在真实走廊数据集上提升了稀疏、遮挡等条件下的稳定性和准确性。
Effective transmission line (TL) inspection in complex corridor environments is essential for ensuring reliable power delivery. This work presents a 3D-based perception method for this task. The proposed method is designed by considering two key characteristics of TL inspection. First, the point cloud data are sparse and class distributions are highly imbalanced, which weakens the signals from thin conductors and tower components. To address this issue, we model long-range spatial relations along the corridor to mitigate data sparsity and imbalance. Second, strong structural correlations exist between conductors and towers, which can be leveraged to improve perception performance. To exploit this property, we construct a unified 3-D representation that jointly models towers, conductors, and vegetation, while fusing Cartesian and polar geometries through geometry-aware alignment. Experiments on real-world corridor datasets demonstrate that the proposed method, termed multigeometric and long-distance TL perception Network (MGLD-TLNet), consistently improves stability and accuracy under conditions of sparsity, occlusion, and complex environmental interactions.