Pavement crack image detection method

An image detection and pavement crack technology, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as pavement interference and detection failure

Inactive Publication Date: 2016-06-29
UNIV OF SHANGHAI FOR SCI & TECH
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  • Application Information

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Problems solved by technology

[0005] The present invention aims at the problems existing in the detection of pavement cracks, and proposes a pavement crack image detection method,

Method used

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  • Pavement crack image detection method
  • Pavement crack image detection method

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Embodiment Construction

[0056] like figure 1 A flow chart of a pavement crack image detection method is shown, and the method specifically includes the following steps:

[0057] Step 1. Process and analyze the collected road surface image. First, convert the image to grayscale and convert the original image from RGB space to grayscale space.

[0058] Step 2, using an image enhancement method based on fuzzy sets to enhance the image, such as figure 2 The effect diagram of image enhancement based on fuzzy sets is shown, that is, the image is changed from the gray space domain to the fuzzy domain, and the membership function value is corrected according to the following analytical formula, and then a new gray level is generated by inverse transformation:

[0059] X = 2 · [ μ ...

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Abstract

The invention relates to a pavement crack image detection method. The method comprises the steps of: carrying out graying and filtering processing on a collected pavement image, constructing a pulse coupling neural network PCNN model, utilizing a genetic algorithm to rapidly find advantages of an optimal solution in a non-linear manner in a solution space so as to optimize important parameters of the model, and rapidly and accurately segmenting cracks and a background in the image; then according to the characteristics of the image after the segmentation, carrying out connected domain detection on the whole image, and filtering out the interference of noise and background textures; and finally, extracting a crack skeleton, calculating the maximum widths of the cracks along the normal line of the skeleton, and making marks in the original image. According to the invention, the digital image processing technology is adopted, the genetic algorithm is utilized to optimize the parameters of the PCNN model, optimization searching is accelerated, the iteration times f the PCNN are reduced, and the iteration is more liable to come to convergence, the interference resistance of the segmentation effect is relatively high, and the segmentation is more accurate; in addition, the modes of connected domain rectangularity, circularity filtering and irregular noise filtering are utilized to filter out a large number of irregular patches, and convenience is provided for the crack detection.

Description

technical field [0001] The invention relates to a pavement detection technology, in particular to a pavement crack image detection method based on a pulse-coupled neural network and a genetic algorithm. Background technique [0002] Whether it is asphalt or cement pavement, after a period of traffic, there will be pavement diseases due to factors such as temperature stress and external environment, and cracks, as a kind of pavement diseases, affect the normal operation of the pavement. Therefore, it is necessary to effectively detect pavement cracks and evaluate their risks so as to avoid potential hazards. [0003] With the development of computer technology and other high-tech fields, it has gradually become the mainstream to identify and detect cracks through digital image detection methods. Its high sensitivity, high automation, and non-contact characteristics provide many conveniences for crack detection. [0004] Some existing crack detection algorithms usually segmen...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T5/10G06T7/00G06N3/12
CPCG06N3/12G06T5/002G06T5/10G06T7/0004G06T2207/20084
Inventor 王艳沈晓宇邹秀阳崔西民谢广苏李德蔺彭水平
Owner UNIV OF SHANGHAI FOR SCI & TECH
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