Intelligent recognition of urban road point clouds based on self-projection attention model
In response to the challenge of inefficient recognition of typical structures in large-scale urban road point cloud environments,this paper proposes a self-projection attention based point cloud recognition model,U-RandLA.The model uses a point cloud projection algorithm to obtain self-projection maps of road point cloud information and employs a two-dimensional image convolutional network branch,U-Proj,to extract features from the self-projection map.Subsequently,it generates attention distribution maps to enhance the recognition capabilities of model for typical structures and improve the focal area perception.By integrating the original point cloud information with features from the attention distribution map,which has a significant receptive field,the model expands its initial receptive field,addressing the narrow receptive field issue of existing algorithms and enhancing the information extraction for large-scale typical structures.Experimental results demonstrate that the U-RandLA model achieves an average recognition accuracy of 97.7％for typical structures,with an average intersection over union of 64.4％.This has contributed to enhancing the production efficiency of practical projects and has been successfully applied to the intelligent extraction of urban road components in Zhejiang Province,Shanghai,Shandong,and Chongqing.
vehicular LiDAR point cloudpoint cloud recognitionRandLA networkpoint cloud projectionattention mechanism
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