CN110751628B - Ultrasonic image-based automatic weld defect identification method - Google Patents
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Abstract
The invention discloses an automatic weld defect identification method based on ultrasonic images. Based on a weldment pairing diagram output by the welding seam ultrasonic detection device, the position of a groove line is automatically detected by utilizing vertical edge detection and Hough transformation, and then the position of the root of the weldment is automatically detected based on groove line distance measurement. And for a weld scanning image output by ultrasonic equipment, obtaining activation regions with different amplitudes by using two-stage threshold segmentation. Then, the number of the two stages of activation regions is counted, the axis of the activation region is obtained by using a method of performing eigenvalue decomposition on a covariance matrix of pixel point coordinates, the position relation between the activation region and a groove and the position relation between the activation region and the root are analyzed, and finally the defect type of the welding line is automatically identified.
Description
Technical Field
The invention belongs to the field of image processing, and relates to an automatic weld defect identification method based on an ultrasonic image.
Background
In recent years, welding techniques have been widely used in manufacturing various structures in the industry, and steel pipes and other welded parts are put into production in large quantities. In any industry, the welding quality has strict standards, and the defects of the welding seam can cause hidden dangers to life or production safety. When the quality evaluation of the welding seam is detected, an ultrasonic mode can be adopted. The moving probe of the ultrasonic wave is used for scanning the weldment along a certain direction, the amplitude of the echo is recorded and processed to generate an image, and then the ultrasonic welding seam images are manually identified and judged to have error defects. With the penetration of artificial intelligence in various industries, the processing and the discrimination of various signals are in transition to automation and intellectualization, but the automatic discrimination of the welding seam ultrasonic image has not made breakthrough progress.
Disclosure of Invention
In order to solve the problems, the invention provides an automatic weld defect identification method based on ultrasonic images.
The invention mainly adopts the technical scheme that:
an automatic weld defect identification method based on ultrasonic images comprises the following specific steps:
step S1: reading a weldment pairing diagram output by ultrasonic equipment, and automatically judging the positions of a groove and a root from the weldment pairing diagram;
step S2: reading an amplitude gray image I output by ultrasonic scanning of a welding seam, wherein the size of the amplitude gray image is recorded as h multiplied by w, h represents the height, and w represents the width; using a threshold value tau 1 Performing primary threshold segmentation to obtain a binary image I 1 ，τ 1 The value range of (a) is 0.75-0.95;
step S3: will correspond to I in I 1 The gray value of the pixel point with the middle value of 1 is set to be 0, and the utilization threshold value of I is tau 2 Performing two-stage threshold segmentation to obtain a binary image I 2 ，τ 2 The value range of (A) is 0.5-0.7;
step S4: to I 1 And I 2 Performing corrosion expansion treatment, statistics I 1 And I 2 The number n of connected regions (called active regions) having a pixel point value of 1 1 ,n 2 ；
Step S5: if n is 1 If yes, go to step S6; if n is 1 If not, turning to step S7;
step S6: if n is 2 If the value is 0, judging that the film is not defective; if n is 2 If the air quantity is more than 0, judging the air bubble;
step S7: if n is 1 If it is 1, go to step S8; if n is 1 If the value is more than 1, turning to the step S11;
step S8: if n is 2 If yes, go to step S9; if n is 2 If the slag content is more than 0, judging the slag is 'strip slag';
step S9: calculation of I 1 The axis of the middle activation region is checked to determine whether the axis is matched with the groove position obtained in the step 1, if so, the defect type of the welding seam is judged to be 'groove unfused', and if not, the step S10 is switched to;
step S10: judging whether the area is consistent with the root position, if so, judging the type of the weld defect as 'root incomplete welding', if not, classifying as 'difficult sample', and waiting for manual treatment;
step 11: the specimen was judged to be "cracked".
The automatic determination of the position of the slope line and the root in the step S1 uses technologies such as edge detection and hough transform, and includes the following specific steps:
step S1.1: binarizing the weldment group diagram by using a threshold value of 0.5, wherein the threshold value range is 0.4-0.6;
step S1.2: and (3) carrying out vertical edge detection on the binary image, wherein the used filter template is as follows:
step S1.3: carrying out Hough transformation on the image of the edge detection result, and searching a peak value in a parameter space after the Hough transformation;
step S1.4: converting the peak value in the parameter space into an original image space to obtain the position of a welded part slope line;
step S1.5: and transversely scanning a result image of the detection of the slope lines, calculating the distance between the slope lines, and marking the position with the shortest distance as the root position of the weldment.
In the step S9, it is determined whether the activation region is consistent with the groove, and techniques such as covariance matrix construction and eigenvalue decomposition are used, and the specific steps are as follows:
step S9.1: constructing a matrix X, wherein the dimension of the matrix is Nx 2, N is the number of pixel points of the activation region, and each row of X stores the transverse coordinate value and the longitudinal coordinate value of a corresponding pixel point respectively;
step S9.2: calculating the average value of the transverse coordinates and the longitudinal coordinates of all coordinate points in the activation region, taking the average value as the coordinate value of the centroid of the activation region, and then subtracting the average value from each row of elements in the X;
step S9.3: solving covariance matrix
Step S9.4: carrying out eigenvalue decomposition on the sigma, and recording the eigenvector corresponding to the maximum eigenvalue as v;
step S9.5: taking the ratio of the second element to the first element in v as a slope, and solving a straight line passing through the centroid to obtain the axis of the activation region;
step S9.5: calculating included angles between the axis and all groove lines and distances from the center of mass to the groove lines, if the included angles are less than 10 degrees and the distances are less than
The axis is considered to coincide with the bevel line, otherwise it is not.
In the step S10, it is determined whether the activation region is matched with the root, and the specific steps are as follows:
step S10.1: calculating the mean value of the transverse coordinates and the longitudinal coordinates of all coordinate points in the activation region as the centroid of the activation region;
step S10.2: if the distance between the area centroid and the root line of the weldment is less than 0.1h, the activated area is judged to be matched with the root of the weldment, otherwise, the activated area is not matched.
The invention has the beneficial effects that:
based on a weldment pairing diagram output by the welding seam ultrasonic detection device, the position of a groove line is automatically detected by utilizing vertical edge detection and Hough transformation, and then the position of the root of the weldment is automatically detected based on groove line distance measurement. And for a weld scanning image output by ultrasonic equipment, obtaining activation regions with different amplitudes by using two-stage threshold segmentation. Then, the number of the two stages of activation regions is counted, the axis of the activation region is obtained by using a method of performing eigenvalue decomposition on a covariance matrix of pixel point coordinates, the position relation between the activation region and a groove and the position relation between the activation region and the root are analyzed, and finally the defect type of the welding line is automatically identified. The system can distinguish normal welding seams and five kinds of defective welding seams: and (3) classifying the samples which cannot be identified by the system into difficult samples to wait for manual treatment due to bubbles, strip slag, cracks, incomplete fusion of grooves and incomplete root penetration.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 illustrates an exemplary pair of weldment pairs;
FIG. 3 vertical edge detection results of a weldment set pair map;
fig. 4 extreme points in the hough transform parameter space;
FIG. 5 shows a detection result of a weld groove line position;
FIG. 6 shows the result of the detection of the root line position of the weldment;
the ultrasound scan amplitude map in the example of fig. 7;
FIG. 8 is a first level thresholding image;
FIG. 9 shows the results of the etching treatment;
FIG. 10 results of the dilation process;
FIG. 11 axis detection results of the activation region.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solution of the present invention is further described in detail with reference to the accompanying drawings, which take an ultrasonic scan image of a certain weldment as an example.
Example (b):
an automatic weld defect identification method based on ultrasonic images is shown in fig. 1, and comprises the following specific steps:
step S1: reading a weldment group diagram output by ultrasonic equipment, as shown in FIG. 2, and automatically judging the positions of a groove and a root from the weldment group diagram;
step S2: reading an amplitude gray image I of a weldment output from an ultrasonic scan, the image having a size of 256 x 400, as shown in FIG. 7, using a threshold τ 1 Performing primary threshold segmentation to obtain a binary image I (0.8) 1 As shown in fig. 8;
step S3: will correspond to I in I 1 The gray value of the pixel point with the middle value of 1 is set to be 0, and the utilization threshold value of I is tau 2 Carrying out two-stage threshold segmentation to obtain a binary image I (0.6) 2 In this case, none of the pixels of the obtained image is 1, which is a full black image;
step S4: to I 1 And I 2 Performing corrosion expansion treatment, statistics I 1 And I 2 The number n of connected regions (called active regions) having a pixel point value of 1 1 ,n 2 In this case I 2 Within no activation zone detected, I 1 The corrosion and expansion results are shown in fig. 9 and 10, respectively;
step S5: if n is 1 If yes, go to step S6; if n is 1 > 0, go to step S7, since n is in this example 1 > 0, subsequently performing step S7;
step S7: if n is 1 If it is 1, go to step S8; if n is 1 If > 1, go to step S11, since n is the case 1 Step S8 is subsequently performed when it is 1;
step S8: if n is 2 If 0, go to step S9; if n is 2 If the value is more than 0, the value is judged to be 'bar slag', since n is in the example 2 When it is 0, step S9 is subsequently performed;
step S9: calculation of I 1 And (3) checking whether the axial line of the activated region is consistent with the position of the groove obtained in the step (1), and identifying the weld defect as 'groove unfused' if the calculation result is consistent.
The specific steps of step S1 are as follows:
step S1.1: binarizing the weldment group by using a threshold value of 0.5;
step S1.2: and (3) carrying out vertical edge detection on the binary image, wherein the used filter template is as follows:
the detection results are shown in FIG. 3;
step S1.3: carrying out Hough transform on the image of the edge detection result, and searching a peak value in a parameter space after Hough transform, wherein the result is shown in FIG. 4;
step S1.4: converting the peak value in the parameter space to the original image space to obtain the position of the weld piece notch line, and the result is shown as a thick line in fig. 5;
step S1.5: the image is scanned transversely, the distance between the groove lines is calculated, and the position with the shortest distance is marked as the root position of the weldment, and the result is shown by the thick line in fig. 6.
The specific steps of step S9 are as follows:
step S9.1: constructing a matrix X, wherein the dimension of the matrix is Nx 2, N is the number of pixel points of the activation region, and each row of X stores the transverse coordinate value and the longitudinal coordinate value of a corresponding pixel point respectively;
step S9.2: calculating the average value of the transverse coordinates and the longitudinal coordinates of all coordinate points in the activation region, taking the average value as the coordinate value of the centroid of the region, and then subtracting the average value from each row of elements in the X;
step S9.3: solving a covariance matrix;
step S9.4: carrying out eigenvalue decomposition on the sigma, and recording the eigenvector corresponding to the maximum eigenvalue as v;
step S9.5: taking the ratio of the second element to the first element in v as a slope, finding a straight line passing through the centroid to obtain an axis of the activation region, wherein the axis is shown in fig. 11;
step S9.5: calculating the included angles between the axis and all the groove lines and the distances from the center of mass to the groove lines, wherein the included angle between the axis and the right groove line is 5 degrees less than 10 degrees, and the distance from the center of mass to the groove line is about 20 degrees and is also less than
The axis is considered to coincide with the bevel line.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (4)
1. An automatic weld defect identification method based on ultrasonic images is characterized by comprising the following specific steps:
step S1: reading a weldment pairing diagram output by ultrasonic equipment, and automatically judging the positions of a groove and a root from the weldment pairing diagram;
step S2: reading an amplitude gray image I output by ultrasonic scanning of a welding seam, wherein the size of the amplitude gray image I is recorded as h multiplied by w, h represents the height, and w represents the width; using a threshold value tau 1 Performing primary threshold segmentation to obtain a binary image I 1 ，τ 1 The value range of (a) is 0.75-0.95;
step S3: corresponding to I in I 1 The gray value of the pixel point with the middle value of 1 is set to be 0, and the utilization threshold value of I is tau 2 Performing two-stage threshold segmentation to obtain a binary image I 2 ，τ 2 The value range of (A) is 0.5-0.7;
step S4: to I 1 And I 2 Performing corrosion expansion treatment, statistics I 1 And I 2 The number n of connected regions with the pixel point value of 1 1 ,n 2 ；
Step S5: if n is 1 If yes, go to step S6; if n is 1 >0, go to step S7;
step S6: if n is 2 If the value is equal to 0, judging that the film is defect-free; if n is 2 >0, judging the bubble;
step S7: if n is 1 If it is 1, go to step S8; if n is 1 >1, go to step S11;
step S8: if n is 2 If yes, go to step S9; if n is 2 >0, judging the slag is 'strip slag';
step S9: calculation of I 1 The axis of the middle activation region is checked whether the position of the groove is consistent with the position of the groove obtained in the step S1, if so, the defect type of the welding seam is judged to be that the groove is not fused, and if not, the step S10 is executed;
step S10: judging whether the area is matched with the root position, if so, judging that the type of the weld defect is that the root is not completely welded, if not, classifying the weld defect as a difficult sample, and waiting for manual treatment;
step 11: and judging the crack.
2. The method for automatically identifying the weld defect based on the ultrasonic image according to claim 1, wherein the step S1 includes the following steps:
step S1.1: binarizing the weldment group diagram by using a threshold value, wherein the range of the threshold value is 0.4-0.6;
step S1.2: and (3) carrying out vertical edge detection on the image obtained by binarization, wherein the used filter template is as follows:
step S1.3: carrying out Hough transformation on the image of the edge detection result, and searching a peak value in a parameter space after the Hough transformation;
step S1.4: converting the peak value in the parameter space into an original image space to obtain the position of a welded part slope line;
step S1.5: and transversely scanning a result image of the detection of the slope lines, calculating the distance between the slope lines, and marking the position with the shortest distance as the root position of the weldment.
3. The method for automatically identifying the weld defect based on the ultrasonic image according to claim 1, wherein the step S9 includes the following steps:
step S9.1: constructing a matrix X, wherein the dimension of the matrix is Nx 2, N is the number of pixel points of the activation region, and each row of X stores the transverse coordinate value and the longitudinal coordinate value of a corresponding pixel point respectively;
step S9.2: calculating the average value of the transverse coordinates and the longitudinal coordinates of all coordinate points in the activation region, taking the average value as the coordinate value of the centroid of the activation region, and then subtracting the average value from each row of elements in the X;
step S9.3: solving covariance matrix
Step S9.4: carrying out eigenvalue decomposition on the sigma, and recording the eigenvector corresponding to the maximum eigenvalue as v;
step S9.5: taking the ratio of the second element to the first element in v as a slope, and solving a straight line passing through the centroid to obtain the axis of the activation region;
step S9.5: calculating included angles between the axis and all groove lines and distances from the center of mass to the groove lines, if the included angles are less than 10 degrees and the distances are less than
The axis is considered to coincide with the bevel line, otherwise it is not.
4. The ultrasonic image-based weld defect automatic identification method according to claim 1, characterized in that: the specific steps of step S10 are as follows:
step S10.1: calculating the mean value of the transverse coordinates and the longitudinal coordinates of all coordinate points in the activation region as the centroid of the activation region;
step S10.2: if the distance between the centroid of the activation area and the root line of the weldment is less than 0.1h, judging that the activation area is matched with the root of the weldment, otherwise, not matching.
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| CN113256566A (en) * | 2021-04-29 | 2021-08-13 | 广州杰赛科技股份有限公司 | Pipeline weld defect identification method |
| CN113658132B (en) * | 2021-08-16 | 2022-08-19 | 沭阳九鼎钢铁有限公司 | Computer vision-based structural part weld joint detection method |
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| CN115861307B (en) * | 2023-02-21 | 2023-04-28 | 深圳市百昌科技有限公司 | Fascia gun power supply driving plate welding fault detection method based on artificial intelligence |
| CN116228703B (en) * | 2023-02-21 | 2024-01-12 | 北京远舢智能科技有限公司 | Defect sample image generation method and device, electronic equipment and medium |
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