Efficient Tree Obstacle Detection and Risk Evaluation for Transmission Lines Using LiDAR and Density-Based Clustering

This study proposes an efficient method for detecting and assessing the risk of tree obstacles near transmission lines using LiDAR point cloud data. Addressing the limitations of traditional inspection methods, the approach integrates point cloud preprocessing, tree identification via DBSCAN clustering, power line detection using RANSAC, and spatial distance-based risk classification. Experimental results on a 35 kV transmission corridor in Guangdong province demonstrate high accuracy and efficiency in identifying high-risk tree locations, offering a reliable tool for preventive maintenance and intelligent vegetation management.