Pavement crack identification and feature extraction algorithm and system

Abstract

The invention discloses a pavement crack identification and feature extraction algorithm and system. The algorithm comprises the following steps: reading a pavement three-dimensional data matrix; performing de-noising processing; identifying a pavement crack to obtain a final crack image; extracting a crack seed point from the crack image and performing region growing to obtain a crack binary image; calibrating the crack with a plurality of identical squares, and framing all the squares with a rectangular frame in such a manner that the area of the rectangular frame is smallest; classifying the crack according to an included angle between the diagonal line of the rectangular frame and a horizontal direction: the crack of which the included angle is within [0, pi / 4] is a transverse crack, otherwise the crack is a longitudinal crack; calculating the length of the diagonal line of the rectangular frame as the length of the crack, and calculating the area of the rectangular frame as the area of a crack region. The crack can be identified and the type of a three-dimensional linear crack can be judged only by inputting the acquired three-dimensional data matrix in order to finish feature extraction; the algorithm and the system have the advantages of easiness in calculation, short running time and accurate detection result.

Description

technical field [0001] The invention belongs to the field of road engineering, and in particular relates to a pavement crack identification and feature extraction algorithm and system. Background technique [0002] According to my country's "Technical Specifications for Highway Maintenance", the evaluation content of my country's expressway pavement includes four parts, namely, the smoothness of the road surface (driving comfort), road damage (pavement damage index), deflection (structural strength of the road surface) and Anti-skid performance (safety). Among them, the Pavement Damage Index (PCI) is the most important data for decision-making and maintenance schemes. It not only reflects the integrity of the pavement structure, but also directly affects the service life of the road. To adopt corresponding maintenance and improvement measures to delay its decay or restore its performance, it is necessary to correctly detect the condition of pavement damage, which is one of t...

AI Technical Summary

Problems solved by technology

However, the two-dimensional images collected in this method are often affected by surrounding environmental conditions (such as road lighting, oil pollution, shadows of buildings and trees, etc.), which greatly interferes with the detection of pavement cracks based on two-dimensional images. It greatly affects the accuracy of pavement crack identification and subsequent crack feature extraction. Therefore, it is necessary to study a pavement crack feature extraction algorithm and system that is less affected by the environment and has high detection accuracy and high efficiency.

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