Method and device for identifying deformation type diseases of pavement

Abstract

The invention provides a method and a device for identifying deformation type diseases of a pavement. The method comprises the steps of S1, according to pre-acquired three-dimensional model of the to-be-tested pavement, acquiring deformation depth information of the to-be-tested pavement; S2, according to the deformation depth information, extracting a suspected disease edge through an edge detecting algorithm; S3, acquiring a preliminary positioning disaster area according to the deformation depth information and the suspected disease edge, and positioning the disease area through area growthrestoring processing; and S4, acquiring the disease attribute set of the disease area, and identifying the disease type of the disease area according to the disease attribute set. According to the method and the device, through acquiring the disease depth information and a deformation depth image, the disease area in the pavement is accurately extracted, thereby ensuring integrity of the diseasearea. Furthermore the disease type of the disease area is accurately identified through the disease attribute, thereby realizing high-efficiency and high-accuracy testing and identification for the deformation type disease of the pavement.

Description

technical field [0001] The invention relates to the technical field of road surface detection, in particular to a method and a device for identifying road deformation diseases. Background technique [0002] With the continuous improvement of economic level, my country's highway construction is developing rapidly. With the continuous increase of traffic volume, people's pursuit of driving comfort and driving safety is increasing day by day. The surface characteristics of roads directly affect people's needs for the above two aspects, which requires continuous improvement of road service levels to ensure excellent road surface characteristics. . [0003] During the use of highways, due to the influence of various natural and unnatural factors, a variety of highway deformation diseases (such as ruts, potholes, pockets, subsidence, etc.) will occur. Therefore, it is very important to quickly and accurately detect the health status of highways and provide road conditions analys...

AI Technical Summary

Problems solved by technology

[0007] However, this processing method is greatly affected by the quality of the collected data, and the false detection rate is high in the process of disease identification; especially for diseases that have no obvious features in the two-dimensional grayscale image (such as some shallow ruts) disease, etc.), therefore, the accuracy of deformation-type disease recognition based on two-dimensional image data is low, and the depth information of deformation-type disease cannot be obtained

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