CN102937592A - Ceramic radome pore and material loosening defect automatic detection method - Google Patents

Abstract

The invention relates to a ceramic radome pore and material loosening defect automatic detection method. The method is characterized in that the detection is carried out with a transmission image processing method. The method comprises the specific steps that: (1) a radome standard grey scale value is obtained; (2) transmission images of different positions of the ceramic radome are obtained; (3) defect area boundary extraction processing is carried out; and (4) a mean gray value and an area of a defect area are calculated; the obtained defect area mean gray value is compared with a mean gray value of the entire image, such that the material loosening state of the defect area can be determined; and the quality of the ceramic radome can be determined with the cooperation of the area value of the defect area. According to the invention, with the image processing automatic detection method, ceramic radome pore and material loosening defect is detected. The detection result is more precise and objective.

Description

Automatic detection method for porosity and material loose defects of ceramic radome

technical field

The invention relates to an automatic detection method for air holes and material loose defects of a ceramic radome, belonging to the technical field of non-destructive testing.

Background technique

After the production of thin-walled ceramic radome products, there are inevitably pores and loose materials. However, in the current inspection of thin-walled ceramic radome products, the detection of pores and loose materials is measured by manual observation. This measurement The result obtained by the method is extremely inaccurate, and the detection quality of the product cannot be guaranteed.

Contents of the invention

The object of the present invention is to provide an automatic detection method for ceramic radome air holes and material loose defects that can overcome the above defects, detect accurately and have high efficiency. Its technical solution is:

An automatic detection method for ceramic radome pores and loose material defects, characterized in that the following steps are adopted: 1) Acquisition of the standard gray value of the radome; 2) Acquisition of the transmission image of the ceramic radome; 3) Extraction and processing of the boundary of the defect area; 4 ) Calculate the average gray value of the defect area and the area of the defect area, and compare the obtained gray value of the defect area with the average gray value of the entire image to judge the looseness of the material of the defect area, combined with the size of the area value of the defect area , to determine the quality of the ceramic radome.

In the method for automatic detection of pores and loose material defects of ceramic radome, in step 1), a light source is set inside the ceramic radome, and a CCD camera is used to automatically collect images outside the ceramic radome to obtain grayscale transmission of different parts of the ceramic radome Image, continuously collect multiple grayscale images of different parts, perform grayscale statistics on each image, calculate the total grayscale value of all images, count the pixel values of each image, and calculate the total number of pixels of all images, by The total gray value is divided by the total number of pixels to get the average gray value of all images, which is used as the standard gray value of the radome.

In the method for automatic detection of air holes and loose material defects of the ceramic radome, in step 2), a light source is set inside the ceramic radome, and a CCD camera is used to collect images of all different parts of the radome outside the radome to obtain different parts of the radome Grayscale transmission image.

In the automatic detection method for air holes and loose material defects of the ceramic radome, in step 3), in order to perform area statistics on the air holes and loose material defect areas, it is necessary to first obtain the boundary of the defect area, so the boundary processing must be performed. In the grayscale image There is a big difference between the gray level of the upper defect area and the background area. The boundary of the defect area is the result of the discontinuity of the gray value of the pixel, so it can be detected by derivation, and the Laplacian operator is used to perform Processing, for a continuous function f(x,y), its Laplace value (ie second derivative) at position (x,y) is defined as:

${\mathrm{<span\; class="notranslate">\; \&Delta;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span>\frac{{<span\; class="notranslate">\; \&PartialD;</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; f</span>}}{{<span\; class="notranslate">\; \&PartialD;</span>\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; +</span>\frac{{<span\; class="notranslate">\; \&PartialD;</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; f</span>}}{{<span\; class="notranslate">\; \&PartialD;</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

Approximated by difference in digital images, one of its expressions is:

Δ ² f＝f(m+1,n)+f(m-1,n)+f(m,n+1)+f(m,n-1)-4f(m,n)

Its template is:

$\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 4</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right]$

The sum of all coefficients of the template is 0, that is, if the value of f(m,n) at each corresponding position of the template is the same (no boundary), the response of the operator is 0.

In the method for automatic detection of pores and loose material defects of ceramic radome, in step 4), after obtaining the boundary of the defect area, the sum of the gray value of each defect area pixel and the total number of pixels are statistically obtained, and then the sum of the gray value of each defect area and respectively divided by the total number of pixels to obtain the average gray level of the defect area, and the area of the defect area is obtained from the total number of pixels of each defect area obtained by statistics, and the average gray level of the defect area is set as T ₂ , in step 1) The calculated average gray value of the entire image is set to T ₁ , then the calculation formula of the material porosity rate δ is as follows:

$\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; \%</span>$

The quality of the ceramic radome is judged by the material porosity δ and the area value of the defect.

Compared with the prior art, the present invention has the advantages that an image processing automatic detection method is adopted to detect the pores and material looseness of the ceramic radome, and the measurement result is more accurate and objective.

Detailed ways

For a ceramic radome to be inspected, the porosity and loose material defects are detected. A light source is set inside the ceramic radome, and a CCD camera is used to automatically collect images outside the ceramic radome to obtain grayscale transmission images of different parts of the ceramic radome. Continuous Collect multiple grayscale images of different parts, perform grayscale statistics on each image, calculate the total grayscale value of all images, count the pixel values of each image, and calculate the total number of pixels of all images, from the total grayscale The value is divided by the total number of pixels to obtain the average gray value of all images, which is used as the standard gray value of the radome. Here, 20 images are collected, and the calculated standard gray value of the radome is: T ₁ =80.

A light source is set inside the ceramic radome, and a CCD camera is used to collect images of all different parts of the radome outside the radome to obtain grayscale transmission images of different parts of the radome, and then undergo the following image processing steps in sequence:

Step 1): After the grayscale image is collected, in order to count the area of the air hole and loose material defect area, the boundary of the defect area needs to be obtained first, so the boundary processing must be performed, and the defect area and the background area on the grayscale image There is a large difference in grayscale. The boundary of the defect area is the result of the discontinuous grayscale value of the pixel, so it can be detected by derivation and processed by the Laplacian operator. For a continuous function f (x,y), its Laplace value (ie second derivative) at position (x,y) is defined as:

${\mathrm{<span\; class="notranslate">\; \&Delta;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span>\frac{{<span\; class="notranslate">\; \&PartialD;</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; f</span>}}{{<span\; class="notranslate">\; \&PartialD;</span>\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; +</span>\frac{{<span\; class="notranslate">\; \&PartialD;</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; f</span>}}{{<span\; class="notranslate">\; \&PartialD;</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

Approximated by difference in digital images, one of its expressions is:

Δ ² f＝f(m+1,n)+f(m-1,n)+f(m,n+1)+f(m,n-1)-4f(m,n)

Its template is:

$\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 4</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right]$

Note that the sum of all coefficients of the template is 0, that is, if the value of f(m,n) at each corresponding position of the template is the same (no boundary), the response of the operator is 0.

Step 2): After obtaining the boundary of the defect area, the sum of the gray values of the pixels in the defect area and the total number of pixels are obtained statistically, and then the sum of the gray values of the pixels in the defect area is divided by the total number of pixels to obtain the average gray value T ₂ = 140, the area of each defect area is obtained from the number of pixels obtained by statistics, and the area is S = 28mm ² , then the calculation formula of material porosity δ is as follows:

$\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 140</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 80</span>}}{\mathrm{<span\; class="notranslate">\; 80</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; \%</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 75</span>}<span\; class="notranslate">\; \%</span>$

The quality of the ceramic radome is judged by the material porosity δ and the area value S of the defect.

Claims (5)

1. a ceramic radome pore and material rarefaction defect automatic testing method is characterized in that adopting image processing method to detect, and concrete steps are: 1) antenna house standard grayscale value obtains; 2) ceramic radome different parts transmission image obtains; 3) the defect area Boundary Extraction is processed; 4) area of calculating defect area average gray and defect area, the defect area gray-scale value and the entire image average gray that obtain compare, be used for judging pore and the loose situation of material of defect area, the area value in binding deficient district is big or small, judges the quality of ceramic radome.

2. ceramic radome pore as claimed in claim 1 and material rarefaction defect automatic testing method, it is characterized in that: in the step 1), in ceramic radome inside light source is set, automatically carry out image acquisition at the outside ccd video camera that adopts of ceramic radome, obtain the gray scale transmission image of ceramic radome different parts, several gray level images of continuous acquisition different parts, every width of cloth image is carried out gray-scale statistical, calculate total gray-scale value of all images, add up the pixel value of every width of cloth image, calculate the total pixel number of all images, obtain the average gray value of all images by total gray-scale value divided by total pixel number, as antenna house standard grayscale value.

3. ceramic radome pore as claimed in claim 1 and material rarefaction defect automatic testing method, it is characterized in that: step 2) in, in ceramic radome inside light source is set, adopt ccd video camera in the antenna house outside the whole different parts of antenna house to be carried out image acquisition, obtain antenna house different parts gray scale transmission image.

4. ceramic radome pore as claimed in claim 1 and material rarefaction defect automatic testing method, it is characterized in that: in the step 3), the gray scale of defect area and background area has larger difference on gray level image, the border of defect area is the discontinuous result of the gray-scale value of pixel, so can it be detected by the mode of differentiate, adopt Laplace operator to process, to a continuous function f (x, y), Laplce's value that it is located at position (x, y) (being second derivative) is defined as:

${\mathrm{\&Delta;}}^{2}f=\frac{{\&PartialD;}^{2}f}{{\&PartialD;x}^2}+\frac{{\&PartialD;}^{2}f}{{\&PartialD;y}^2}$

Be similar to difference in digital picture, its expression is:

Δ ²f＝f(m+1,n)+f(m-1,n)+f(m,n+1)+f(m,n-1)-4f(m,n)

Its template is:

$\left[\begin{array}{ccc}0& 1& 0\\ 1& -4& 1\\ 0& 1& 0\end{array}\right]$

All coefficient sums of template are 0, that is, if in the value identical (not having the border) of each corresponding position f (m, n) of template, then the response of operator is 0.

5. ceramic radome pore as claimed in claim 1 and material rarefaction defect automatic testing method, it is characterized in that: in the step 4), after obtaining the defect area border, statistics obtains gray-scale value sum and the total pixel number of each defect area pixel, then by the gray-scale value sum respectively divided by total pixel number, obtain the average gray of defect area, the total pixel number of each defect area that is obtained by statistics obtains the area of defect area, and the average gray of defect area is made as T ₂, the average gray of the entire image of calculating in the step 1) is made as T ₁, then the computing formula of the loose rate δ of material is as follows:

$\mathrm{\&delta;}=\frac{T_2-T_1}{T_1}\&times;100\%$

Judged the quality of ceramic radome by the area value of the loose rate δ of material and defective.