A Calculation Method of 3D Pavement Disease pci Based on Graph-Point-Graph Transformation

Abstract

The present invention is a PCI calculation method for three-dimensional pavement diseases based on graph-point-graph transformation. The calculation method can directly calculate the PCI of three-dimensional pavement diseases through RGB images. The point map transformation converts the RGB image into a grayscale map containing distance information, extracts the three-dimensional features of the three-dimensional disease by processing the two-dimensional grayscale map, and performs damage classification and PCI calculation. The whole process of the calculation method is to identify and process the two-dimensional image, which saves a lot of calculation time and computing power compared with the three-dimensional network processing three-dimensional data. Setting the GA-CNN algorithm reduces the workload and provides an accurate volume calculation method to generate The grayscale map has higher precision, which can reach submillimeter pixel precision, and realizes rapid identification of 3D disease types, precise extraction of 3D morphological features of diseases, and efficient classification of disease damage degrees.

Description

technical field [0001] The invention relates to a PCI calculation method for three-dimensional pavement damage based on graph-point-graph transformation. Background technique [0002] In order to ensure the safe driving of vehicles on the road and prolong the service life of the road, it is necessary to regularly inspect and maintain the road. The pavement performance index has decisive significance for the decision-making of the pavement maintenance plan, so the calculation of the pavement performance index is very necessary. Pavement Condition Index (PCI) is an index for evaluating the damage degree of road pavement, which integrates the quantitative status of three aspects: damage type; damage degree; damage range or density, etc., and uses the deduction method for analysis. Pavement damage evaluation and maintenance decision analysis are shown in Table 1: [0003] [0004] The type of disease, the degree of damage and the damage range (area) of the disease are the th...

AI Technical Summary

Problems solved by technology

For the method of manual identification and measurement of diseases, the detection even needs to close the road, which will consume a lot of manpower, material and financial resources, and the detection efficiency is difficult to guarantee

Manual detection is easily affected by subjective judgment and lacks consistent judgment standards

The acquisition equipment such as laser or radar carried by the road inspection vehicle is expensive, and the collected data is distance information, which cannot realize the calculation of the road damage index

Traditional digital graphics can only obtain texture information of pavement damage, but cannot obtain high-precision three-dimensional damage depth, damage volume and other three-dimensional features of damage. At present, there is no high-precision identification of three-dimensional damage and extraction of three-dimensional damage features and calculation of road surface through digital images. Actionable Methodology for Condition Index (PCI)

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