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Self-adaptive segmentation method based on crack image subarea gray scale mean value

A gray-scale average and self-adaptive technology, applied in image analysis, image enhancement, image data processing, etc., can solve the problems of low algorithm complexity and poor reliability

Active Publication Date: 2015-08-26
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] In order to overcome the disadvantages of poor reliability of existing crack edge detection methods, the present invention provides an adaptive segmentation based on crack image sub-area gray mean value with wide application range, low algorithm complexity, simple implementation and fast operation speed method, does not use any filter denoising and edge enhancement processing methods, but directly performs image segmentation and recognition on the original image

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  • Self-adaptive segmentation method based on crack image subarea gray scale mean value

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Embodiment Construction

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] refer to Figure 1 ~ Figure 4 , an adaptive segmentation method based on the average gray level of crack image sub-regions, including the following steps:

[0038] 1) Analyze the crack image features.

[0039] The gray histogram of a digital image is a function of the gray level of the image, which describes the number of pixels of each gray level in the image, reflects the frequency of each gray level in the image, and is the main tool for analyzing image features. A digital image histogram with a gray level in the range [0,L-1] is a discrete function h(r k ) = n k , the normalized histogram is P(r k ) = n k / n, where n is the total number of pixels in the image, n k is the gray level in the image is r k The number of pixels, r k is the kth gray level, According to this definition, programming calculates the gray histogram h(r) for different regions o...

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Abstract

Disclosed is a self-adaptive segmentation method based on a crack image subarea gray scale mean value. The method comprises the following steps: 1), analyzing crack image characteristics on the basis of crack image gray scale statistical data of an image histogram; 2), selecting a narrow and long rectangular area as an image analysis ROI area, wherein the narrow and long rectangular area is symmetrical about a prefabricated crack expansion horizontal line and comprises fatigue cracks; 3), dividing the ROI area into two rows of multiple same-dimensioned square area image subareas from a prefabricated crack starting point to a crack right edge position; 4), calculating a grayscale distribution variance for each image subarea, and according to a variance distribution rule, excluding image subareas not including the fatigue cracks; 5), performing histogram calculation on the image subareas comprising the cracks to obtain a histogram two-dimensional array; and 6), performing crack segmentation on each subarea by use of a self-adaptive iteration method based on the crack image subarea gray scale mean value. According to the invention, the crack measuring precision is improved, the application scope is wide, the algorithm complexity is low, the realization is simple, and the operation speed is fast.

Description

technical field [0001] The invention relates to the field of image segmentation of image processing technology, in particular to an adaptive segmentation method based on the mean value of the gray level of a crack image sub-area to realize crack edge detection for subsequent crack length measurement. Background technique [0002] As we all know, machine vision technology based on image processing is applied to the measurement field of industrial products. While ensuring the measurement requirements, it makes the system non-contact, adaptable, fast, efficient, accurate, flexible, high reliability, easy operation and low cost. Low-level characteristics, the crack detection method using image processing technology can complete the crack detection and further analysis and processing intuitively, accurately, in real time and reliably. [0003] In order to ensure the accuracy of fatigue crack size measurement, the position of the crack edge must be strictly guaranteed in the image...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0008G06T7/11G06T7/13G06T2207/30108
Inventor 高红俐郑欢斌刘辉朱亚伦杨庆华
Owner ZHEJIANG UNIV OF TECH
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