基于图神经网络的手机扫描点云路面坑洞检测方法OA北大核心

Rapid detection and assessment of road surface potholes are essential for traffic safety.To address the high cost and limited applicability of current detection methods based on survey vehicles or drones,as well as the low quantitative accuracy of smartphone-based approaches,this study proposes a novel method for pothole extrac-tion and quantification from smartphone-scanned point clouds using a graph-based attention neural network(GANN).Road surface point cloud data are collected using a LiDAR-equipped smartphone via circular scanning and are preprocessed through planar fitting and clustering algorithms.To effectively capture the local geometric fea-tures characteristic of pothole structures,a deep learning model is developed based on graph attention mechanisms,extending traditional graph neural network(GNN)models.The proposed GANN model introduces an Attention Neighbor Convolution Layer,which identifies key neighboring nodes within an expanded receptive field using atten-tion mechanisms,addressing limitations associated with dynamic graph construction present in existing approaches.Additionally,a Geometric Feature Extractor is designed by incorporating an umbrella surface representation to accu-rately characterize local geometric structures that are often overlooked by prior methodologies.These architectural enhancements enable high-precision classification and quantitative analysis of the preprocessed point cloud data.Ex-periments were conducted using an iPhone 14 Pro to scan road surface potholes around the Yujiatou Campus of Wu-han University of Technology in Wuchang District,Wuhan,resulting in a real-world urban road pothole point cloud dataset.Results show that the proposed GANN model achieves a depth quantification error of 4.58%and a volume quantification error of 5.57%,demonstrating its effectiveness in extracting potholes from point cloud data.Com-pared with state-of-the-art models such as PointNeXt and PointMLP,GANN reduces depth and volume quantifica-tion errors by 2.41%and 0.11%,respectively,offering superior accuracy in pothole quantification through improved information retention and geometric feature extraction.