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Highway pavement diseases characteristic extracting method based on sparse resolution theory

A technology of sparse decomposition and feature extraction, which is applied in the direction of measuring devices, instruments, character and pattern recognition, etc., can solve the problems of unsatisfactory practical application effect and insufficient feature extraction, and achieve improved efficiency, flexible structure, improved accuracy and speed Effect

Inactive Publication Date: 2009-08-19
HARBIN INST OF TECH
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  • Abstract
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Problems solved by technology

[0012] In order to solve the problem of insufficient feature extraction and unsatisfactory practical application effect in the contour signal analysis technology based on structured light, the present invention proposes a road surface disease feature extraction method based on sparse decomposition theory

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  • Highway pavement diseases characteristic extracting method based on sparse resolution theory
  • Highway pavement diseases characteristic extracting method based on sparse resolution theory
  • Highway pavement diseases characteristic extracting method based on sparse resolution theory

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specific Embodiment approach 1

[0028] Specific implementation mode one: the steps of the road pavement disease feature extraction method based on the sparse decomposition theory in this implementation mode are as follows:

[0029] Step 1: Establish an atomic library of pavement disease characteristics , according to different disease characteristics, different disease characteristic atomic libraries are established, and the position and scale are used as parameters, and the parameters are changed in different ranges, and the atoms are normalized to obtain an over-complete atomic library of disease characteristics;

[0030] Step 2: According to the signal expansion theory, select K atoms from the over-complete atomic library to perform K-item approximation on the signal, and then select the atom with the sparsest decomposition coefficient from various possible combinations of K atoms according to the sparse decomposition theory Combination; coefficient C of disease characteristics k The choice of needs to ...

specific Embodiment approach 2

[0035] Embodiment 2: This embodiment is a further description of Embodiment 1. Formulas 1-3 in Step 2 of this embodiment are extracted using the framework method in the sparse decomposition theory, FOCUSS, base tracing or matching pursuit. Other steps are the same as in the first embodiment.

specific Embodiment approach 3

[0036] Specific implementation mode three: in this embodiment, the steps of extracting crack disease features through the highway pavement disease feature extraction method based on sparse decomposition theory are as follows:

[0037] Step 1: Establish an atomic library of crack disease characteristics Such as formula 1-1:

[0038]

[0039] where a k Indicates the location information of the crack, b k Represents the scale information of the fracture, that is, the width information; k represents a series of fracture types with different positions and different widths; A k is the normalization coefficient, such as formula 1-2, so as to ensure that atoms have unit energy, which is convenient for subsequent processing;

[0040] A k = 1 | | exp ( - ( ...

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Abstract

The invention discloses a method for extracting road surface disease features based on the sparse decomposition theory and relates to the road surface disease detection technique, which solves the problems that the contour signal analysis technique based on structured light has insufficient feature extraction and unsatisfactory actual application effect and the like. The method comprises the following steps of: firstly, establishing different disease feature atom dictionary according to different disease features, taking position and scale as parameters which change in a different ranges, and normalizing the atoms, thus obtaining a complete disease feature atom dictionary; secondly, according to the signal spreading theory, selecting K atom pair signals from the complete disease feature atom dictionary to carry out K approximations, and then selecting an atom combination with a most sparse decomposition coefficient from the K atom combinations according to the sparse decomposition theory. The selection of the coefficient Ck of the disease feature should satisfy a sparse constraint condition which is as follows:* C 0 s. t f =*Ck Phi k; the disease feature can be expressed as: f (t) = fk + Sigma = Sigma Ck Phi k (t)+ Sigma, wherein k = 0, 1, 2, and other integers; and Sigma is an approximate error. The method for extracting road surface disease features is used for detecting road surface disease features, such as crackles, tracing ruts, pits or earth bulges, and the like.

Description

technical field [0001] The invention relates to a road surface disease detection technology, in particular to a method for extracting road surface disease features. Background technique [0002] With the vigorous development of the expressway industry, pavement disease detection has become an important research topic in the highway maintenance industry, and occupies an important position in the highway maintenance. The traditional manual inspection technology is slow, inaccurate and affects traffic, which can no longer meet the requirements of road inspection development. The development of high-performance computer and image processing technology makes the automatic detection of pavement disease possible, and the detection of pavement has always been a research hotspot. However, the pavement image has problems such as uneven gray scale, light source, shadow, stability, etc., and its complexity, diversity, and weak signal of disease information cause many problems in the au...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06K9/62G01B21/00
Inventor 刘宛予李新红孙晓明黄建平
Owner HARBIN INST OF TECH
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