Keywords: 2D wavelet transform template pattern recognition
Foreword
Pipeline transportation is a safe, economical, and environmentally friendly way of transport. In the next 10 years, China will build 14 oil and gas transmission pipelines, forming “two vertical, two horizontal, four-hub, and five-gas reservoirs†with a total length of more than 10,000 kilometers. The pattern of oil and gas pipeline transmission. The construction area of ​​the pipeline is large, and the construction environment along the line is harsh. In addition, the pipeline transportation gradually develops toward high pressure (7.5 MPa) and large bore (1 420 mm), which imposes higher requirements on the welding of the pipe's circumferential welds. The welding of welds has become a key process that restricts the quality of the entire project and the construction cycle.
In view of the potentially huge market for oil and gas pipeline construction worldwide, in order to improve the quality of welds, reduce the labor intensity of workers, realize the automation of pipeline construction field operations, and shorten the construction period, many well-known pipeline companies at home and abroad have conducted automatic pipeline welding technology and equipment. The development and research.
It is well known that the automatic tracking of pipe loops is the precondition for automatic welding, and the extraction of circular seam information is a necessary condition for automatic tracking. A study on the identification of pipeline circular slots for this problem was conducted.
Currently, methods based on image gray-level mutations are commonly used in image processing of welds. This kind of method does not apply to the case where the gradient of the edge of the groove is not obvious and there is a lot of noise interference on the surface of the workpiece. Pattern recognition is a new subject rapidly developed in the early 60s, belonging to the category of information, control and systems science. With the development of large-scale integration technology and the rapid improvement of computer's cost-effectiveness, pattern recognition technology has developed significantly in both theory and application, promoting the development of academic orientation and new technologies such as image recognition. The pattern is understood to be a synthesis of the measured values ​​of a single sample taken from a finite part of the world; pattern recognition is the attempt to determine the class attribute of a sample, that is, to assign a sample to one of multiple models [1]. Pattern recognition system should complete the functions of pattern acquisition, feature extraction/selection and classification. For the ring groove, although the texture features of the circular groove of different pipes are different, the texture features of the same ring groove are basically the same, so a corresponding template is established for each ring groove, and the template is used. The matching method identifies the circular groove. First, the two-dimensional wavelet transform method is used to process the initial groove image, and the wavelet transform result is binarized to obtain the texture information of the image. Then the optimal wavelet transform scale is determined by the optimization algorithm, and the wavelet transform result is determined at this scale. The binary image is a template; Wavelet transform and binarization processing on the same scale are performed on the subsequent image, and the template is used to perform template matching calculation on the subsequent image after the binarization processing to determine the position of the follow-up image groove, and finally output Subsequent image groove center data. In a nutshell, identification of the circular groove is divided into four steps.
(1) Acquisition of groove images.
(2) establish a circular groove template.
(3) Identify the ring groove.
(4) Output groove center position data.
1 Bevel image acquisition
In view of various visual sensors of welding robots, CCD sensors have the advantages of reliable performance, clear and intuitive images, and easy use. This article uses the area array CCD to obtain the weld groove image. The Matrox Meteor-II/Standard image acquisition card converts the acquired analog image into a digital signal and sends it to the computer. Figure 1 shows an image of the ring groove obtained when the torch is centered properly in the initial state.
Figure 1 One frame weld groove image
2 Create Weld Groove Formwork
2.1 Initial conditions for identification of circular groove
According to the geometric characteristics of the circular groove of the pipe and the characteristics of the CCD camera assembly on the pipe welding robot, the following initial conditions can be determined:
(1) The direction of the weld bevel is basically vertical.
(2) The welding torch is facing the center of the bead in the initial state, and the center of the CCD camera is consistent with the center of the torch.
(3) Weld groove width is known.
2.2 Using Wavelet Transform to Extract the Texture Information of Ring Slot Groove Image
Many of the important features in the image are highly localized in spatial locations. These components are not similar to any one of the Fourier basis functions, and their transform coefficients are not compactly distributed. This makes the Fourier transform and other transformation methods not be optimally represented when analyzing signals and images that contain transient or localized components. For this reason, mathematicians and engineers have developed several methods for transforming using a finite-width basis function. These basis functions vary not only in frequency but also in position. They are waves of finite width and are called wavelets. Transforms based on them are called wavelet transforms [2]. Since the cardinal B-spline function can be said to be the simplest function with small support for software or hardware implementation, B-spline wavelet is used to extract the texture information of the groove image. Take the scale of 2m three directions wavelet as shown below
(1)
In the formula 
, 
, 
—The first, second, and third direction wavelets with n times, respectively
m - Binary Scale Factor - An nth-order 2D B-spline function with a scale of 2m.
Corresponding to the three directions in the frequency domain, the wavelet is defined as
(2)
In the formula 
- Fourier transformation of n-dimensional 2D B-spline functions
Wx, wy - real frequency corresponding to x and y axes, respectively
- A B-spline function with frequency n is corresponding to the frequency domain representation of the first, second and third direction wavelets
G(1) is the transfer function of canonical Canny operator B-spline one-dimensional wavelet FIR filter coefficient g(1), G(2) is a classifier LoG operator B-spline one-dimensional wavelet FIR filter coefficient g(2) The transfer function.
g(1) takes: g1=-1, g2=1, gk=0 k≠1, 2
g(2): g1=1, g2=-2, g3=1, gk=0
k≠1, 2, 3
Then, the recursive algorithm for calculating the local partial derivative of the {2m}m∈Z binary sequence is:
(3) Where - 2m low-pass smoothing of image matrix f
— Wavelet transform with a 2m scale for the image matrix f using the first, second, and third direction wavelets, respectively
It means that the rows and columns of the matrix after smoothing with the image matrix f at the 2m-1 scale are respectively convolved with the one-dimensional filter coefficients h. The smoothing filter coefficient h takes: h2 = 0.0625, h3 = 0.25, h4 = 0.375, h5 = 0.25, h6 = 0.0625, hk = 0 (k≠2, 3, 4, 5, 6). The symbol d represents the Dirac filter coefficient, which is 1 at the origin and 0 elsewhere.
Take the original image acquired by the CCD camera and image card.
Since the groove is in the vertical direction, wavelet transform of Figure 1 is performed using the first direction wavelet. The result is shown in the following figure.
(a) The wavelet scale is 21 (b) The wavelet scale is 22
Fig. 2 Wavelet transform results of Fig. 1 using wavelets of different scales
Binarize the results of wavelet changes
(4)
(5)
In the formula 
, 
- a first direction with f wavelet transform for wavelet scales 21 and 22
T1 , T2 - binary images corresponding to a and b in Figure 2
- The value at the (i,j) point after the wavelet transform with dimensions 21 and 22 of the first direction wavelet on the weld bevel image f
T1, t2 - thresholds corresponding to Figure 2a, b
f - indicates Figure 1
Where t1 and t2 respectively pass through the pair of transformation matrices 
and 
The absolute value of each data in the sum is summed to obtain the average.
The resulting binary image is shown below
Figure 3 Binary image corresponding to Figure 2
2.3 Determine Groove Image Template
As can be seen from Fig. 3, not all binarized images of wavelet transform results are suitable as templates. According to the initial conditions, this paper establishes a template selection objective function:
Min Vm-∑Tt(x1,x2)+x3×0.2×Vm (6)
St Vm-∑Tt(x1,x2)≤0.2×Vm
X1=Hc
x2≤Vm
X2≥1
Where Vm - the maximum value of the ordinate of the binary image
Tt - binary image
X1 - binary image abscissa
X2 - binary image ordinate
X3 - number of times
Hc - point value in the abscissa of the binary image
Binarize the result of the wavelet transform to calculate that the sum of 1 pixel on the centerline of the binary image is 1 pixel and > 0.8×Vm?
2m-scale wavelet transform of the original image
m=1,x3=0
X3=x3+1
Determine the current binary image as a template
m=m+1
N
Y
The significance of the objective function is to perform the least number of wavelet transforms on the image under the condition that the length of the centerline of the binary image reaches more than 80% of the image length, so as to reduce the calculation time. The flow chart of the optimization algorithm is shown in Figure 4. The final template is shown in Figure 5. In order to improve the pattern recognition accuracy, instead of taking the entire binary image as a template, one section is taken and the template size is taken as 100×280 (pixels).
3Using template matching method to identify circular groove
The template identified above is used to identify the weld bevel by the template matching algorithm. The algorithm flow chart is shown in Figure 6. The calculation result is shown in Fig. 7. As can be seen from Fig. 7, if there is a peak in the graph, the image area corresponding to the peak is the groove area of ​​the section in the image.
4 position data output
4.1 Constraints There is no abrupt change in the horizontal direction deviation between the torch and the center of the groove. The absolute value of the center deviation of the groove at two successive images is less than L (as determined by actual conditions).
4.2 Calibration and welding gun position data output is shown in Fig. 8. Take the center of the image (corresponding to the horizontal position of the welding gun) as a reference, -L corresponds to 8-bit serial output minimum value 0, and the image center corresponds to 8-bit serial output. The middle value, 2L length corresponds to the maximum value of the 8-bit serial output. Calculate the current horizontal position difference between the center of the groove and the reference and convert it into serial data and output it through the serial port.
N
S(j)=Tp and Ts(1:100, j:(j+Stc)) Cross-Correlation Calculation Results
j=j+1
j
Y
Subsequent image wavelet transform and binary processing to obtain binary image Ts; initialization j=1
J—column S—an array of cross-correlation values ​​at each corresponding position when the template is translated in a subsequent binary image Tp—template Ts—successive bevel binarization image Stc—the total number of templates Sbc—binary Total number of images
5 Conclusion
(1) Using the two-dimensional wavelet transform method, abrupt texture information of the weld bevel image in the horizontal and vertical directions can be obtained.
(2) For a different groove, a corresponding dynamic template is generated in the initial state of welding, and the template is matched with the bevel image of the bead to calculate the basic position of the bevel and its centerline in the image.
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