基于结构频谱感知框架的配电网点云语义分割OA

The semantic segmentation of point clouds in power distribution networks is of great significance for en-abling unmanned inspection and intelligent grid operation and maintenance.Although existing methods have made some progress in spatial modeling and structural enhancement,they still face prominent challenges in spectral fea-ture extraction and the efficiency of large-scale point cloud processing.To address these issues,this paper proposes a structure spectrum-aware framework(SSAF)to enhance the expressive capability of point clouds in long-distance distribution network scenarios.In the data preprocessing stage,a structure-guided hierarchical filtering strategy and a structure-aware sample partitioning method are designed to reduce redundant background points while preserving the structural integrity and continuity of key objects such as poles and wires.During the semantic segmentation stage,a spatial-spectral collaborative semantic segmentation network is constructed,in which local polar coordin-ates are introduced to enhance direction-sensitive feature modeling.Furthermore,a dynamic fusion mechanism based on attention maps is employed to enable adaptive interaction and information enhancement between spatial and spectral features.Experimental results show that SSAF achieves higher segmentation accuracy and inference ef-ficiency on real-world distribution network point cloud datasets.It outperforms existing representative methods across multiple key metrics,demonstrating its practicality and engineering generalization potential in complex scen-arios.