Method for automatically identifying pavement diseases

Abstract

The invention discloses a method for automatically identifying pavement diseases. The method comprises the following steps of 1, converting an input pavement grey image into a binary image; 2, using a digital filter template for performing expansion processing and corrosion processing on the binary image obtained in the step 1; performing eight-communication mark on the image obtained in the step 2, obtaining the height and width of each communication area, and setting the communication area with the maximum value of the height and width smaller than the first preset threshold value to be black; 4, performing linear fitting on each communication area of the image obtained in the step 3, obtaining the length and direction vectors of a fitting line segment, obtaining the communication areas where the fitting segments with the length larger than the second preset threshold value are located, and taking the communication areas as seed areas; 5, obtaining confidence coefficients of all extended seed areas, if the maximum confidence coefficient is smaller than the confidence coefficient threshold value, judging that the diseases are not found in the pavement grey image, and if the maximum confidence coefficient is larger than a confidence coefficient threshold value, judging that the diseases are found in the pavement grey image.

Description

technical field [0001] The invention belongs to the technical field of non-destructive detection and evaluation of highway subgrade pavement, and in particular relates to an automatic identification method for asphalt pavement disease (for example, crack). Background technique [0002] During the use of highways, affected by many factors such as the natural environment and driving loads, various damages are gradually formed on the surface of the highway. Cracks, as a common form of damage to the road surface, are extremely harmful to the highway. In order to save maintenance resources and ensure the safety and comfort of driving, it is necessary to accurately obtain parameter information such as the location, area, and degree of cracks, so as to provide a basis for the traffic management department to objectively evaluate the quality of the road surface and make scientific decisions on maintenance management plans. [0003] At present, with the development of sensor, automat...

AI Technical Summary

Problems solved by technology

[0010] Various road conditions and the high noise and weak signal characteristics of road images, the existing methods generally have the problems of complex calculation and low recognition efficiency

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