Part segmentation of transmission tower based on laser point cloud deep learning
For the UAV electric power inspection process of transmission tower refined inspection,the existing tower parts point cloud extraction accuracy is low,and it is difficult to meet the UAV autonomous inspection route planning as well as digital information management.In this paper,segmentation of transmission tower components using deep learning method based on point cloud data and proposed PCTTS model to realize accurate extraction of each component of point cloud transmission tower.Firstly,the raw transmission corridor point cloud data were subjected to data preprocessing,and the towers with complete data quality were used as samples.Secondly,the transmission tower point cloud data is sampled from an octree,and the point cloud is thinned while retaining as many local features as possible.Finally,the samples are fed into the deep learning model,which combines multi-scale feature extraction strategy and Offset-attention mechanism while ensuring translation and rotation invariance to complete feature extraction and propagation,and realize the segmentation of the parts of the point cloud transmission towers.The experimental results show that the model achieves mIOU of 94.1％on the self-built part segmentation dataset,and the segmentation accuracy outperforms that of the methods for point cloud segmentation,such as PointNet,PointNet++,DGCNN,Point Transformer,and PointMLP.
deep learningLiDARpoint cloudtransmission towerpart segmentationattention mechanism
NETL
NSTL
万方数据
