Backpack LiDAR damage pavement deformation detection method

doi:10.13474/j.cnki.11-2246.2024.0710

Abstract

Abstract: In order to efficiently monitor the road damage phenomenon, this paper adopts backpack 3D laser scanning technology to observe the road surface, and proposes a method to detect the deformation of damaged road surface. For the stretching type of damage, a point cloud damaged pavement extraction algorithm based on the mean value of the colour field is established. Firstly, a two-dimensional grid is constructed for the initial extraction of damage, and then the weighted mean value of the RGB colour gamut of the point cloud is calculated, and the point cloud of road damage is obtained by fine screening through the differentiation of the ratio; for the extrusion-type damage, the normal vector pinch angle standard deviation method is used. Then, using Euclidean clustering, the classified tensile and extruded damages are calculated as length, width, damage interval, height, and finally the pavement inclination. A comprehensive pavement damage evaluation model is established through the 5D information. The experimental results show that the accuracy of tensile damage extraction reaches 96.4%, and the accuracy of extrusion damage extraction reaches 100%, and the comprehensive evaluation model of pavement damage indicates that the road is a third-class damage, which needs to be repaired. It basically meets the required accuracy of road damage extraction, and provides technical support for the formulation of road traffic control and repair measures.

Key words: damage detection, colour gamut weighting, 2D grid, normal vectors, comprehensive evaluation model of pavement damage

CLC Number:

WU Jingdong, CAI Lailiang, ZHANG Bingjie, WANG Xin. Backpack LiDAR damage pavement deformation detection method[J]. Bulletin of Surveying and Mapping, 2024, 0(7): 55-59,82.

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