Point Cloud Semantic Segmentation of Distribution Net‍work Tower Based on Self-Attention Perception

doi:10.3969/j.issn.1008-0198.2024.06.011

Abstract

Abstract: Automatically and accurately extracting power towers from airborne laser point cloud data in power distribution scenes is a critical step in routine power inspections. Due to the undulating terrainand dense vegetationin mountainous areas,the features are difficult to distinguish, and it remains challenging to extract targets from mountainous power scenes using existing methods. To solve this problem, a semantic segmentation method based on self-attention perception is proposed,which mainly includes color feature extraction,local position encoding,and multi-layer attention perception modules. When processing large-scale point clouds of distribution network scenes, this method can integrate RGB features as attached attributes of the point cloud's spatial features,thereby improving the accuracy of semantic segmentation. Experimental results show that the proposed method has significant performance advantages in power scenarios, achieving a point cloud segmentation accuracy of over 90%, and its effectiveness has been validated on the public S3DIS dataset.

Key words: power inspection, RGB, laser point cloud, semantic segmentation, deep learning

CLC Number:

TM754

HUANG Zhihong, LIU Yu, ZHANG Hui, XU Xianyong, PENG Jinzhu. Point Cloud Semantic Segmentation of Distribution Net‍work Tower Based on Self-Attention Perception[J]. Hunan Electric Power, 2024, 44(6): 76-82.

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