Optimization method of point cloud data processing for hybrid transmission line inspection

ZHANG Ruizhi; LI Qiang; ZHANG Xiaolin

doi:10.7688/j.issn.1000-1646.2025.04.06

Abstract：[Objective]Due to the long-term exposure of overhead transmission lines to the natural environment and the significant impact of environmental factors,timely monitoring of their operating status plays a key role in the safe operation of the power grid.With the development of UAV flight control technology and the widespread use of detection technologies such as infrared,ultraviolet,and LiDAR,these methods are increasingly used in the inspection of power transmission lines.However,traditional methods currently only show optimal results in single-scenario line inspection.In more complex environments,such as mixed transmission line inspections,it is challenging to quickly and accurately analyze transmission line inspection data.Therefore,this study proposed an optimization method for point cloud data processing in hybrid transmission line inspection.[Methods]First,a transmission line inspection point cloud data processing platform was constructed.LiDAR mounted on the UAV platform collected the mixed point cloud data of the transmission line and processed it in four stages:data management,preprocessing,classification,and intelligent inspection.The mixed point cloud data were thinned using the octree method to reduce redundant data and ensure the accuracy and quality of the data.Finally,a neural network model was designed to optimize the sparse data,consisting of three main parts:the feature learning layer,the convolutional layer,and the classification layer.The feature learning layer avoided the impact of the disorder in 3D point cloud data on feature extraction through multiple projections and maximum pooling.The convolutional layer extracted common features from voxel grids and surrounding entities while incorporating traditional transmission line feature extraction algorithms to extract voxel grid features.The classification layer included a fully connected layer with a ReLU activation function,using the Softmax model as the classification function to obtain the classification results of the mixed point cloud data.[Results]In the experiment,the LDLRS3100 LiDAR was selected to collect point cloud data of a transmission line channel in a certain area.The UAV LiDAR system has a range of 360 m,a flight speed of 20 km/h,and a flight altitude of 150 m.The proposed method was analyzed based on the Pytorch platform,and the results show that it can effectively identify the differences between transmission lines and ground objects,and obtain clear information on the tower and its surrounding environment.The overall accuracy reaches 92.71％,which is significantly better than other comparative methods.To balance the highest sparsity rate and the best visual effect of the point cloud data,the sparsity density is set to 0.02 m.[Conclusion]By optimizing the point cloud data of mixed transmission line inspections using the octree sparsity method and a neural network model,various types of point cloud data can be quickly and accurately classified,thus improving the reliability of intelligent transmission line inspections.