CN101135652A - Weld joint recognition method based on texture partition - Google Patents

Abstract

The method comprises: using CCD video camera to shoot the image containing the weld metal zone; analyzing the texture feature of the weld metal zone and the base material zone; according to difference in the texture zone between the weld metal zone and the base material zone, distinguishing the weld metal zone; finally, extracting the edge of the weld metal zone to get the edge of weld metal zone.

Description

Weld joint recognition method based on Texture Segmentation

Technical field

The invention belongs to welding quality detects and the control technology field automatically.Relate to based on the weld seam recognition of visual pattern Texture Segmentation and a kind of weld joint recognition method of tracking technique based on Texture Segmentation, but aspects such as widespread use and robot automation's welding.

Background technology

The automatic identification and the weld joint tracking of weld seam have critical role in the welding robot intelligent development.And commonly used and practical be to realize by vision, mainly comprise two kinds of methods: initiatively light vision and passive smooth vision.Initiatively the light vision adopts active illuminating devices such as laser scanning, structured light to form a bright striped that comprises the groove shape information on weld groove, and this method system is complicated, cost is higher.Passive vision is to rely under natural light or the arc light condition, obtains the image that comprises weld seam, by Flame Image Process, obtains the edge of weld seam, and this method usually needs weld image to have tangible gray scale sudden change feature.

For thick plates, usually adopt multi-run welding, multilayer welding method, at first in groove, carry out backing welding during welding, adopt then and fill weldering filling groove, when filling weldering, can adopt multi-run welding as required, tend to carry out cosmetic welding at last.Behind the backing welding, carrying out along with welding process, the feature of weld groove is more and more not obvious, promptly be unfavorable for structure light vision method and general passive vision method: the Three Dimensions Structure of groove is not obvious, make structured light on groove, can not form and have the bright striped of obvious turnover, thereby be difficult for to determine the weld seam center, and be subjected to very easily to splash in the weld seam next door, greasy dirt etc. influences; Weld edge does not have tangible shade of gray on the weld image, can not realize determining weld edge by simple image processing method (as edge extracting, gray level threshold segmentation).

Summary of the invention

The objective of the invention is to overcome the prior art deficiency, proposed weld joint recognition method, so that be implemented in the weld seam recognition problem before the cosmetic welding in the multilayer welding based on the weld image Texture Segmentation.

In order to realize this purpose, in the technical scheme of the present invention, at first use ccd video camera to take the image that comprises welded seam area, the textural characteristics in welded seam area in the analysis image and mother metal zone then, difference according to welded seam area and mother metal regional texture feature is distinguished welded seam area, and the edge that extracts welded seam area at last is weld edge.

Weld joint recognition method based on Texture Segmentation of the present invention mainly comprises following step.

1) Image Acquisition is used ccd video camera to obtain and is comprised weld seam at interior image, makes the image middle part be welded seam area, and the welded seam area both sides are the mother metal zone.

2) texture feature extraction, the image that step 1 is obtained carries out the textural characteristics analysis, and the textural characteristics of extraction can adopt statistical nature or spectrum signature to analyze; Described statistical nature is commonly used to be represented based on the feature descriptor of gray level co-occurrence matrixes; Spectrum signature then obtains by the Gabor wavelet transformation.The extraction of statistical nature is that first image with described step 1 carries out the subimage division, and the size of subimage determine-promptly makes each subimage embodiment image texture features and subimage size can guarantee the edge accuracy of image segmentation again according to image texture characteristic and weld seam recognition accuracy requirement; Then through type (1) calculate each number of sub images co-occurrence matrix M (h k), characterizes the textural characteristics of this subimage again with the textural characteristics descriptor of co-occurrence matrix, textural characteristics descriptor commonly used have the expression energy second moment W _M, contrast W _C, entropy W _E, unfavourable balance square W _HDeng, shown in (2)～(5); During concrete the use, can select one or more suitable uses; It is that original image is resolved into a plurality of channels by wavelet transformation that spectrum signature extracts, wherein the Gabor small echo can adopt Gabor function suc as formula (6) as basic small echo, can be after the parameter of, translation flexible and the direction discretize suc as formula the discrete wavelet family of (7) basic small echo, corresponding wavelet transformation is as the formula (8); On these channels, calculate texture energy again and characterize textural characteristics, be defined in the feature e that is called " texture energy " in (2u+1) * (2v+1) window (i, j) as the formula (9).

Wherein, (x y) is the weld seam subimage to f, h, k image f (x, the y) gray-scale value of middle pixel, the quantity of # represent pixel.

$W_M=\underset{h}{\mathrm{\Σ}}\underset{k}{\mathrm{\Σ}}{M}^2(h,k),---\left(2\right)$

$W_C=\underset{h}{\mathrm{\Σ}}\underset{k}{\mathrm{\Σ}}|h-k|M(h,k),---\left(3\right)$

$W_E=-\underset{h}{\text{\Σ}}\underset{k}{\mathrm{\Σ}}M(h,k)\log (h,k),---\left(4\right)$

$W_H=\underset{h}{\mathrm{\Σ}}\underset{k}{\mathrm{\Σ}}\frac{M(h,k)}{1+|h-k|}\·---\left(5\right)$

g _λ(x，y，θ)＝exp[-(λ ²x′ ²+y′ ²)+iπx′]， (6)

x′＝xcosθ+ysinθ，y′＝-xsinθ+ycosθ，

Wherein λ is the aspect ratio of x and y direction, and θ is a direction parameter.

g(α′(x-x ₀，y-y ₀)，θ _k)，α∈R，j＝{0，-1，-2，…}， (7)

θ in the formula _k=k π/N, k={0 ..., N-1}, N represent the number of discrete direction.

W ₁(x，y，θ)＝∫f(x ₁，y ₁)g ^*(α′(x-x ₀，y-y ₀)，θ)dx ₁dy ₁ (8)

$e(i,j)=\frac{1}{{(2u+1)}^2}\underset{m=i-u}{\overset{i+u}{\mathrm{\Σ}}}\underset{n=j-u}{\overset{j+u}{\mathrm{\Σ}}}\left|W(m,n)\right|,---\left(9\right)$

Wherein, m, n, i, j are image coordinate, (m n) carries out result behind the wavelet transformation for image to W.

3) Texture Segmentation.According to the otherness of welded seam area and mother metal regional texture feature, selected threshold value is carried out Threshold Segmentation to the textural characteristics that calculates, and image is divided into welded seam area one black sign and mother metal zone one white sign, obtains the preliminary segmentation result of image.

4) cut apart elimination and weld edge determines by mistake.According to continuous, the small curve characteristics of weld edge, eliminate the zone that by mistake is identified as weld seam in the mother metal zone, and definite weld edge.Concrete grammar is as follows, in tentatively being defined as the subimage of welded seam area, search for weld edge to the middle part respectively from the image both sides, be enough to down with discontented that the subimage of characteristics changes to the mother metal zone, with the subimage of the following characteristics of first fit as weld edge: (1) this subimage along weld seam longitudinally 8-neighborhood internal memory be the subimage that belongs to welded seam area; (2) in an image-region that with this subimage is the center, the quantity that belongs to the subimage of welded seam area accounts for the ratio more than 50%, and the weld edges that both sides are all couple together separately, the edge that promptly to form two black lines be weld seam.

The weld joint recognition method that the present invention proposes based on Texture Segmentation, utilize the difference of the textural characteristics in welded seam area and mother metal zone, can realize the weld seam recognition problem, particularly the weld seam recognition for filling weldering in the multilayer welding and cosmetic welding has clear superiority than method of structured light and general passive smooth visible sensation method.

Description of drawings

Fig. 1 is based on the weld joint recognition method flow process of Texture Segmentation.

Fig. 2 is that the subimage of weld image is divided.

Fig. 3 is that image texture features to be identified distributes.

Fig. 4 is the Texture Segmentation result.

Fig. 5 is that weld edge extracts the result.

Embodiment

In order to explain technical scheme of the present invention better, be described in further detail below in conjunction with embodiment.

Figure 1 shows that weld joint recognition method flow process of the present invention, comprise following step:

1, Image Acquisition is used ccd video camera to obtain and is comprised weld seam at interior image, makes the image middle part be welded seam area, and the welded seam area both sides are the mother metal zone.

2, texture feature extraction, the image that step 1 is obtained carries out the textural characteristics analysis, obtains image texture features; The textural characteristics that extracts comprises statistical nature and spectrum signature.This example adopts based on energy (second moment) W in the textural characteristics descriptor of gray level co-occurrence matrixes _MRepresent textural characteristics.At first determine the subimage size, and original image carried out area dividing that divide the result as shown in Figure 2, this example adopts the subimage size of 24 * 10 (the vertical pixel counts of weld seam * weld seam horizontal pixel number) according to image pixel and relation in kind; Calculate the co-occurrence matrix of each subimage then, the calculating parameter of co-occurrence matrix is gray level 32, gray scale step-length 1, direction 0 degree, and promptly (x, y) being transformed to gray level is 32 image, calculates M (h, k) pixel (x in the seasonal formula (1) with subimage f earlier ₁, y ₁), (x ₂, y ₂) satisfied (x ₂=x ₁+ 1, y ₂=y ₁+ 1); (h k) calculates the textural characteristics value (representing with second moment) of each number of sub images with formula (2), the result as shown in Figure 3 based on this co-occurrence matrix M again.

3, Texture Segmentation.According to the otherness of welded seam area and mother metal regional texture feature, selected threshold value T ₀=0.13, the textural characteristics value among Fig. 3 is cut apart, the textural characteristics value is designated the mother metal zone greater than the subimage of threshold value, the textural characteristics value is designated welded seam area smaller or equal to the subimage of threshold value, the preliminary segmentation result of image, as shown in Figure 4.

4, cut apart elimination and weld edge determines by mistake.According to continuous, the small curve characteristics of weld edge, eliminate the zone that by mistake is identified as weld seam in the mother metal zone, and definite weld edge.Concrete grammar is as follows, in tentatively being defined as the subimage of welded seam area, respectively to middle part search weld edge, is enough to down with discontented that the subimage of characteristics changes to the mother metal zone from the image both sides, with the subimage of the following characteristics of first fit as weld edge:

(1) 8 exist along the both direction of weld seam of this subimage are belonging to the point of welded seam area;

(2) be in one 5 * 3 of the center (weld seam vertically * weld seam is horizontal) image-region with this subimage, the quantity that belongs to the subimage of welded seam area is occupied the ratio greater than 50%.The weld edges that both sides are all couple together respectively, promptly form two edges of weld seam.

According to mentioned above principle, as shown in Figure 4, leftmost 3 welded seam areas that are identified as black do not satisfy characteristics (1) and characteristics (2) among the figure, so should change the mother metal zone into; Outstanding two number of sub images zone, the rightmost side is not owing to satisfy characteristics (2) so change the mother metal zone into yet among the figure; With the high order end of the remaining subimage left side edge as weld seam, low order end obtains weld edge as shown in Figure 5 as the right side edge of weld seam afterwards.

Claims (6)

1. the weld joint recognition method based on Texture Segmentation is characterized in that, the image that comprises welded seam area that obtains during to welding carries out the textural characteristics analysis, according to the difference identification welded seam area of welded seam area and mother metal regional texture feature, and definite weld edge.

2. according to the described weld joint recognition method of claim 1, it is characterized in that, may further comprise the steps based on Texture Segmentation:

1) Image Acquisition is used ccd video camera to obtain and is comprised weld seam at interior image, makes the image middle part be welded seam area, and the welded seam area both sides are the mother metal zone;

2) texture feature extraction, the image that step 1 is obtained carries out the textural characteristics analysis, and the textural characteristics of extraction adopts statistical nature of representing based on the feature descriptor of gray level co-occurrence matrixes or the spectrum signature that obtains by the Gabor wavelet transformation to extract;

3) Texture Segmentation: according to the otherness of welded seam area and mother metal regional texture feature, selected threshold value, the textural characteristics that calculates by step (2) carries out Texture Segmentation to the image of step (1), tentatively determines welded seam area and mother metal zone;

4) cut apart elimination and weld edge determines by mistake: according to continuous, the small curve characteristics of weld edge, eliminate the zone that by mistake is identified as weld seam in the mother metal zone, and definite weld edge.

3. the weld joint recognition method based on Texture Segmentation according to claim 2, it is characterized in that, it is that first image with described step (1) carries out the subimage division that described statistical nature extracts, calculate the co-occurrence matrix of each number of sub images then, textural characteristics descriptor with co-occurrence matrix characterizes textural characteristics again, and textural characteristics descriptor commonly used has the second moment W of expression energy _M, contrast W _C, entropy W _E, unfavourable balance square W _HDeng, as follows suc as formula the co-occurrence matrix that calculates each number of sub images shown in (1)～(5):

$M(h,k)=\frac{\#\left\{\right[(x_1,y_1),(x_2,y_2)]\∈S|f(x_1,y_1)=h\&f(x_2,y_2)=k\}}{\#S},---\left(1\right)$

Wherein, (x y) is the weld seam subimage to f, h, k image f (x, the y) gray-scale value of middle pixel, the quantity of # represent pixel;

$W_M=\underset{h}{\mathrm{\Σ}}\underset{k}{\mathrm{\Σ}}{M}^2(h,k),---\left(2\right)$

$W_C=\underset{h}{\mathrm{\Σ}}\underset{k}{\mathrm{\Σ}}|h-k|M(h,k),---\left(3\right)$

$W_E=-\underset{h}{\mathrm{\Σ}}\underset{k}{\mathrm{\Σ}}M(h,k)\log M(h,k),---\left(4\right)$

$W_H=\underset{h}{\mathrm{\Σ}}\underset{k}{\mathrm{\Σ}}\frac{M(h,k)}{1+|h-k|},---\left(5\right)$

4. the weld joint recognition method based on Texture Segmentation according to claim 2, it is characterized in that, it is that original image is resolved into a plurality of channels by wavelet transformation that described spectrum signature extracts, wherein the Gabor small echo can adopt Gabor function suc as formula (6) as basic small echo, can be after the parameter of, translation flexible and the direction discretize suc as formula the discrete wavelet family of (7) basic small echo, corresponding wavelet transformation is as the formula (8); On these channels, calculate texture energy again and characterize textural characteristics, be defined in the feature e that is called " texture energy " in (2u+1) * (2v+1) window (i, j) as the formula (9), each expression formula is as follows:

g _λ(x，y，θ)＝exp[-(λ ²x′ ²+y′ ²)+iπx′]， (6)

x′＝xcosθ+ysinθ， y′＝-xsinθ+ycosθ，

Wherein λ is the aspect ratio of x and y direction, and θ is a direction parameter.

g(α′(x-x ₀，y-y ₀)，θ _k)，α∈R，j＝{0，-1，-2，…}， (7)

θ in the formula _k=k π/N, k={0 ..., N-1}, N represent the number of discrete direction;

W _j(x，y，θ)＝∫f(x ₁，y ₁)g ^*(α ^j(x-x ₀，y-y ₀)，θ)dx ₁dy ₁. (8)

$e(i,j)=\frac{1}{{(2u+1)}^2}\underset{m=i-u}{\overset{i+u}{\mathrm{\Σ}}}\underset{n=j-u}{\overset{j+u}{\mathrm{\Σ}}}\left|W(m,n)\right|,---\left(9\right)$

Wherein, m, n, i, j are image coordinate, (m n) carries out result behind the wavelet transformation for image to W.

5. according to claim 2,3 or 4 described weld joint recognition methods based on Texture Segmentation, it is characterized in that, described step (3) is selected threshold value, according to the subimage textural characteristics of described step (2) acquisition and the magnitude relationship of threshold value, the subimage on the original image is identified into welded seam area and mother metal zone respectively.

6. the weld joint recognition method based on Texture Segmentation according to claim 2, it is characterized in that, described step (4), its method tentatively is defined as in described step (3) in the subimage of welded seam area, respectively to middle part search weld edge, the subimage that does not satisfy edge feature is changed to the mother metal zone from the image both sides, with the subimage of first fit edge feature as weld edge, again that both sides are all weld edges couple together separately, promptly form two edges of weld seam; Wherein edge feature is: 1) to exist in the 8-neighborhood longitudinally along weld seam be the subimage of weld edge to this subimage; 2) in an image-region that with this subimage is the center, the quantity that belongs to the subimage of welded seam area is occupied at least 50% ratio.

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