Road surface damage detection method based on grid model and related equipment

Abstract

The invention discloses a road surface damage detection method based on a grid model and related equipment. The method comprises the following steps: carrying out point cloud sampling based on an adaptive grid model of a triangular patch area; segmenting a multi-scale point cluster according to the sampled grid model point cloud, analyzing the size of the point cluster, extracting a ground point cluster, and generating a road surface reference surface; identifying points in the negative direction of the reference surface, performing European clustering on the points to generate a point cluster, judging the size of the point cluster, identifying road surface damage, extracting damage point cloud, and obtaining a live-action grid model at the road damage position through an index relationship between the sampling point cloud and the grid model; calculating the distance from the middle point of the damage point cloud to the road surface reference surface, obtaining the collapse degree quantitative data of the damage part, projecting the damage point cloud to the reference surface, fitting the optimal outsourcing ellipse of the damage after projection, obtaining the relative long axis, short axis and area of the road surface damage, and completing the complete quantitative evaluation of the road surface damage part.

Description

technical field [0001] The invention relates to the technical field of road damage detection, in particular to a grid model-based road surface damage detection method and related equipment. Background technique [0002] Road construction is one of the important indicators to measure the level of national infrastructure construction. With the continuous development of the logistics industry and the continuous growth of vehicle ownership, the problem of road damage has become increasingly prominent. How to quickly find road surface damage, and accurately identify and quantitatively evaluate the damage area is of great significance. [0003] At present, the method of identifying road damage based on two-dimensional images and performing quantitative assessment is relatively mature. Relevant technicians have proposed a variety of methods for practical use. However, road damage is usually a kind of collapse damage, and it is necessary to accurately quantitatively assess the degr...

AI Technical Summary

Problems solved by technology

[0006] The main purpose of the present invention is to provide a road surface damage detection method, system, terminal and computer-readable storage medium based on a grid model, aiming at solving the problem that road damage detection algorithms in the prior art are usually based on deep learning. The algorithm is still based on two-dimensional images, which can only be used to detect damage, but cannot perform quantitative evaluation on damage, and this type of method requires a large number of training samples, which does not meet the needs of practical applications.

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