CN107248170B - Weld joint tracking method based on image matching - Google Patents

Weld joint tracking method based on image matching Download PDF

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CN107248170B
CN107248170B CN201710272076.3A CN201710272076A CN107248170B CN 107248170 B CN107248170 B CN 107248170B CN 201710272076 A CN201710272076 A CN 201710272076A CN 107248170 B CN107248170 B CN 107248170B
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filter
image
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welding seam
tracking
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CN107248170A (en
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张涛
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Liaoning Normal University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G06T7/248—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments involving reference images or patches
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
    • B23K9/00—Arc welding or cutting
    • B23K9/12—Automatic feeding or moving of electrodes or work for spot or seam welding or cutting
    • B23K9/127—Means for tracking lines during arc welding or cutting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0014—Image feed-back for automatic industrial control, e.g. robot with camera
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection

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Abstract

The invention discloses a weld joint tracking method based on image matching, which comprehensively considers the weld joint area, a background image and the characteristic difference between the weld joint area and the background image, is not limited by the depth and the thickness of a weld joint and the like, is not influenced by environmental conditions such as illumination and the like, solves the problems of weld joint tracking loss and the like caused by gradual change of visual characteristics of the weld joint along with imaging conditions such as illumination and the like, and has high universality and higher robustness and accuracy.

Description

Weld joint tracking method based on image matching
Technical Field
The invention relates to a welding seam tracking method, in particular to a welding seam tracking method based on image matching, which is not limited by welding seams and environmental conditions and has higher robustness and accuracy.
Background
At present, the weld joint tracking method based on a visual sensor mainly comprises the following two methods: one method is based on active light, namely, light bars generated by an auxiliary light source are projected onto a welding seam to generate deformation, and the center of the welding seam is found by using an image processing technology to track. The light strip is not easy to deform when the welding line is small and shallow, so that the method has poor tracking effect on fine welding lines, is unstable in tracking when the outside interferes with the auxiliary light source, and is complex to implement and high in cost; the other method is based on passive light, namely the diffuse reflection imaging detection weld seam tracking of the surface of a workpiece is caused by utilizing ambient natural light or the weld seam tracking is directly carried out by utilizing a welding pool image, and specifically comprises a template matching tracking method and an edge detection tracking method. The template matching tracking method mainly uses the gray value characteristics of the template to find the region with the minimum difference with the gray value of the template in the weld image for weld matching tracking, has the advantages of simple operation, low cost and the like, but is extremely sensitive to illumination, slow in tracking speed and poor in anti-interference capability; the edge detection tracking method is to utilize the edge of the welding seam for tracking the welding seam, and the method is not easily affected by illumination and has higher tracking speed, but has poor tracking effect when the edge of the welding seam is not obvious or the edges around the welding seam are more.
Disclosure of Invention
The invention aims to solve the technical problems in the prior art and provides the weld joint tracking method based on image matching, which is not limited by weld joints and environmental conditions and has higher robustness and accuracy.
The technical solution of the invention is as follows: a welding seam tracking method based on image matching is characterized in that the method comprises an initial welding seam filter training stage and an on-line tracking stage,
the initial weld filter training phase is carried out according to the following steps:
A1. getkA sample image;
A2. manually marking and extracting a welding seam area in the sample image;
A3. automatically intercepting areas on two sides of a welding seam area as background images;
A4. respectively extracting the characteristics of a welding seam area and a background image;
A5. calculating an initial weld filter;
A6. storing the obtained initial weld filter;
the on-line tracking stage is carried out according to the following steps:
B1. manually setting coordinates of initial position of weld center in image
Figure DEST_PATH_IMAGE001
And order the initial search center
Figure DEST_PATH_IMAGE002
；
B2. Acquiring an initial weld filter;
B3. collecting a detected image in real time;
B4. extracting coordinates from the detected image
Figure 891478DEST_PATH_IMAGE001
A rectangular area as a center is used as a search area;
B5. extracting the characteristics of the search area;
B6. calculating the maximum response value of the filter and the feature of the search area
Figure DEST_PATH_IMAGE003
And
Figure 770921DEST_PATH_IMAGE003
position coordinates in an image
Figure DEST_PATH_IMAGE004
；
B7.1 and B7.2 were performed simultaneously:
b7.1.1 calculation
Figure 170678DEST_PATH_IMAGE004
And
Figure DEST_PATH_IMAGE005
pixel deviation of (2);
b7.1.2 pixel biasing into stack;
b7.1.3 judging whether the stack is full; calculating deviation control quantity, outputting the deviation control quantity to a motion control module, releasing a stack, and updating
Figure 440248DEST_PATH_IMAGE002
(ii) a If not, update
Figure DEST_PATH_IMAGE006
；
B7.1.4 judging whether the tracking is finished; NO, return to B3; if yes, the tracking is finished;
b7.2.1 in coordinates
Figure 837731DEST_PATH_IMAGE004
Collecting background images of a welding seam area and two sides of the welding seam area for the center;
b7.2.2 extracting the characteristics of the weld region and the background image respectively;
b7.2.3 calculating the maximum response value of the background image feature and the filter
Figure DEST_PATH_IMAGE007
；
B7.2.4 judgment
Figure DEST_PATH_IMAGE008
(ii) a If yes, updating the filter; if not, it is determined whether or not tracking is completed, and if yes, tracking is completed, otherwise, the routine returns to B3.
The method comprehensively considers the weld joint characteristics, the background characteristics and the characteristic difference between the weld joint characteristics and the background characteristics, is not limited by the depth, the thickness and the like of the weld joint, is not influenced by environmental conditions such as illumination and the like, solves the problems of weld joint tracking loss and the like caused by gradual change of the visual characteristics of the weld joint along with imaging conditions such as illumination and the like, and has high universality and higher robustness and accuracy.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a schematic diagram of the effect of seam tracking according to an embodiment of the present invention.
Detailed Description
The hardware part of the embodiment of the invention is the same as the prior art, namely comprises an image acquisition module (a CCD camera, an image acquisition card and the like), a motion control module, a power transmission module, a welding module and the like. The weld tracking method based on image matching of the embodiment of the invention is composed of an initial weld filter training stage and an online tracking stage as shown in fig. 1.
The initial weld filter training phase is carried out according to the following steps:
A1. getkA sample image: from the training set, selecting
Figure 255068DEST_PATH_IMAGE009
The web contains an image of the weld;
A2. and (3) manually marking and extracting a weld joint region in the sample image: selecting a part capable of showing weld joint characteristics from each image as a weld joint area, wherein the weld joint area selection requires that: taking the shape of a rectangle with a length of
Figure DEST_PATH_IMAGE010
Wide is
Figure DEST_PATH_IMAGE011
Ground rectangular region, the side of the rectangle perpendicular to the direction of the weld
Figure 520833DEST_PATH_IMAGE011
Shorter, parallel to the edges of the weld
Figure 582592DEST_PATH_IMAGE010
The length is longer; the center of the welding seam is positioned in the center of the rectangular frame, and then the welding seam area sample set
Figure DEST_PATH_IMAGE012
；
A3. Automatically intercepting areas on two sides of a welding seam area as background images;
in the region of the weld seam
Figure 541190DEST_PATH_IMAGE013
Two sides of the image are respectively and automatically picked up to be used as background images, wherein the image is the same as the size of a welding line area
Figure DEST_PATH_IMAGE014
Representing the identity of the weld region, then the background image sample set
Figure DEST_PATH_IMAGE015
；
A4. Respectively extracting the characteristics of a welding seam area and a background image;
FHOG and gray features of the weld region and the background image sample are extracted respectively, wherein the FHOG features are extracted by the method disclosed in the document "Object detection with discrete transformed part-based models, PAMI, 32(9): 1627-. And sequentially combining 27 characteristic maps formed by FHOG characteristics of each sample image and one gray characteristic map as the characteristics of the sample. By using
Figure DEST_PATH_IMAGE016
Is shown as
Figure DEST_PATH_IMAGE017
First of a weld zone
Figure DEST_PATH_IMAGE018
The characteristic diagram, in the same way,
Figure DEST_PATH_IMAGE019
is shown as
Figure 930976DEST_PATH_IMAGE017
Second of background image
Figure 875799DEST_PATH_IMAGE018
A feature map;
A5. calculating an initial weld filter:
calculating a weld filter according to equation (1)
Figure DEST_PATH_IMAGE020
Figure DEST_PATH_IMAGE021
（1）
Wherein
Figure DEST_PATH_IMAGE022
，
Figure DEST_PATH_IMAGE023
，
Figure DEST_PATH_IMAGE024
；
Figure DEST_PATH_IMAGE025
Representing a cross-correlation;
Figure DEST_PATH_IMAGE026
representing the response of the welding seam filter and the welding seam, and being a Gaussian distribution window with the same size as the image of the welding seam area;
Figure DEST_PATH_IMAGE027
representing the response of the welding seam filter and the background, and being a matrix composed of small numbers, wherein the matrix is an all-0 matrix;
Figure DEST_PATH_IMAGE028
、
Figure DEST_PATH_IMAGE029
、
Figure DEST_PATH_IMAGE030
is a weight factor representing three terms;
the specific calculation process is as follows:
selecting a Gaussian distribution window with the same size as the image of the welding seam area as the output of the filter
Figure 406444DEST_PATH_IMAGE026
Require
Figure 411309DEST_PATH_IMAGE026
The peak value of the template image is just positioned at the central position of the template image, and the position is the preset central position of the welding seam;
in the frequency domain, the filter is applied to the formula (1) relative to the weld seam
Figure DEST_PATH_IMAGE031
Calculating the deviation, inputting the image characteristics of the welding seam
Figure DEST_PATH_IMAGE032
And its expected output
Figure 537659DEST_PATH_IMAGE026
Features of the background input image
Figure 958277DEST_PATH_IMAGE019
And its expected output
Figure 56945DEST_PATH_IMAGE027
And a filter
Figure 232711DEST_PATH_IMAGE031
After FFT respectively represented as
Figure DEST_PATH_IMAGE033
、
Figure DEST_PATH_IMAGE034
、
Figure DEST_PATH_IMAGE035
、
Figure DEST_PATH_IMAGE036
And
Figure DEST_PATH_IMAGE037
the frequency domain representation of the initial weld filter is obtained as follows:
Figure DEST_PATH_IMAGE038
（2）
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE039
representing the filter in the frequency domain
Figure 659407DEST_PATH_IMAGE018
A layer of a material selected from the group consisting of,
Figure DEST_PATH_IMAGE040
represents a complex conjugate;
A6. saving the resulting initial weld filter
Figure 8348DEST_PATH_IMAGE039
；
The on-line tracking stage is carried out according to the following steps:
B1. manually setting coordinates of initial position of weld center in image
Figure 633627DEST_PATH_IMAGE001
And order the initial search center
Figure 980295DEST_PATH_IMAGE002
Manually adjusting the motion control module to enable a welding gun (the welding gun is bound with a camera) to be aligned with the center of a welding seam; meanwhile, marking the initial position of the center of the welding seam in the image shot by the camera, and recording the coordinate of the initial position
Figure 455139DEST_PATH_IMAGE005
；
B2. Obtaining an initial weld filter: obtaining an initial filter calculated through an initial weld filter training phase
Figure 545454DEST_PATH_IMAGE037
；
B3. Collecting a detected image in real time;
B4. extracting coordinates from the detected image
Figure 290819DEST_PATH_IMAGE001
The rectangular area at the center serves as a search area:
for an image acquired in real time, in coordinates
Figure 542808DEST_PATH_IMAGE001
Is central, width and height are respectively
Figure 504948DEST_PATH_IMAGE011
And
Figure 133376DEST_PATH_IMAGE010
as a search area for the center of the weld in the image, said
Figure 998826DEST_PATH_IMAGE011
And
Figure 421717DEST_PATH_IMAGE010
the value is the same as the value of the initial welding seam filter in the training stage;
B5. extracting the characteristics of the search area:
extracting FHOG characteristics of the search area, and forming the characteristics of the image by sequentially forming the 27-dimensional FHOG characteristics and the gray scale map
Figure DEST_PATH_IMAGE041
And performing discrete Fourier transform to obtain
Figure DEST_PATH_IMAGE042
；
B6. Calculating the maximum response value of the filter and the feature of the search area
Figure 508970DEST_PATH_IMAGE003
And
Figure 941088DEST_PATH_IMAGE003
position coordinates in an image
Figure 894001DEST_PATH_IMAGE004
；
The obtained characteristics
Figure 753373DEST_PATH_IMAGE042
Correlation with the filter (equation 2) to obtain the response
Figure DEST_PATH_IMAGE043
The formula is as follows:
Figure DEST_PATH_IMAGE044
（3）
wherein the content of the first and second substances,
Figure 988307DEST_PATH_IMAGE043
is the response output of the spatial domain, then
Figure 224116DEST_PATH_IMAGE043
Maximum value of
Figure 297115DEST_PATH_IMAGE003
The position in the image coordinate is the central position of the welding seam, and the coordinate of the position is recorded as
Figure 828852DEST_PATH_IMAGE004
；
Then B7.1 and B7.2 were performed simultaneously:
b7.1.1 calculation
Figure 987301DEST_PATH_IMAGE004
And
Figure 761222DEST_PATH_IMAGE005
pixel deviation of (2);
b7.1.2 pixel offset into the stack: the stack layer is set according to the system output interval, and the embodiment of the invention is set to be 7 layers;
b7.1.3 judging whether the stack is full; removing the maximum value and the minimum value in the deviation values, averaging the rest deviation values to be used as motion deviation control quantity, and outputting the motion deviation control quantity to a motion control module so as to control the motor to start to move and further control the welding gun and the welding gunThe camera moves, then the stack is released, and the center coordinates of the search area are updated
Figure 688727DEST_PATH_IMAGE002
(ii) a If not, update
Figure 391366DEST_PATH_IMAGE006
；
B7.1.4 judging whether the tracking is finished; NO, return to B3; if yes, the tracking is finished;
b7.2.1 in coordinates
Figure 37111DEST_PATH_IMAGE004
Acquiring background images of the welding seam area and two sides of the welding seam area for the center: the specific method is the same as the initial welding seam filter training stage;
b7.2.2 extracting the features of the weld region and the background image respectively: the specific method is the same as the initial welding seam filter training stage;
b7.2.3 calculating the maximum response value of the background image feature and the filter
Figure 349144DEST_PATH_IMAGE007
: the calculation formula is the same as formula (3), and the maximum response value of the background image characteristic and the filter is obtained
Figure 334417DEST_PATH_IMAGE007
；
B7.2.4 judgment
Figure 706493DEST_PATH_IMAGE008
(in the invention)
Figure 340998DEST_PATH_IMAGE009
= 1.2); if yes, update the filter:
if it is not
Figure 456722DEST_PATH_IMAGE045
When the maximum response value of the weld joint area is greatly different from the maximum response value of the background image, the weld joint is clear and obvious, and the filter is updated at the moment, namely the filter is updated, namelyThe filter is modified to the weighted sum of the currently calculated filter and the previous filter, i.e.:
Figure DEST_PATH_IMAGE046
（4）
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE047
is shown as
Figure DEST_PATH_IMAGE048
First of frame time filter
Figure 47234DEST_PATH_IMAGE018
The number of the main components is one,
Figure DEST_PATH_IMAGE049
is shown as
Figure DEST_PATH_IMAGE050
First of frame time filter
Figure 216310DEST_PATH_IMAGE018
An
Figure DEST_PATH_IMAGE051
Is an indication function, if
Figure DEST_PATH_IMAGE052
If true, then
Figure 633385DEST_PATH_IMAGE051
Equal to 1; otherwise, it equals 0.
If it is not
Figure DEST_PATH_IMAGE053
If the difference between the maximum response value of the welding seam region and the maximum response value of the background image is not very large, the welding seam is not obvious, and the filter does not need to be updated; it is judged whether or not tracking is finished, yes, tracking is finished, no, return to B3.
The experimental results are as follows:
taking two weld experiments as an example, as shown in fig. 2, the error value between the weld tracking result of the experiment and the standard result is shown by a dashed line (zero error) as a reference, and the average error obtained is 0.553 and 0.347 pixels respectively, and the experimental result proves the robustness and the accuracy of the invention.

Claims (3)

1. A welding seam tracking method based on image matching is characterized in that the method comprises an initial welding seam filter training stage and an on-line tracking stage,
the initial weld filter training phase is carried out according to the following steps:
A1. taking k sample images;
A2. manually marking and extracting a welding seam area in the sample image;
A3. automatically intercepting areas on two sides of a welding seam area as background images;
A4. respectively extracting FHOG characteristics and gray characteristics of a welding seam area and a background image, sequentially combining 27 characteristic graphs and a gray characteristic graph formed by the FHOG characteristics of each sample image as the characteristics of the sample, and using the characteristics
Figure FDA0002481837270000011
The ith feature map showing the ith weld zone,
Figure FDA0002481837270000012
an ith feature map representing an ith background image;
A5. calculating h-h of the weld filter according to a formula1,h2,…,h28}
Figure FDA0002481837270000013
Wherein
Figure FDA0002481837270000014
Figure FDA00024818372700000110
Representing a cross-correlation; g is a Gaussian distribution window with the same size as the image of the welding seam area; b0α, β and gamma are weight factors representing three items;
the specific calculation process is as follows:
the peak value of the g is required to be just positioned at the central position of the template image, and the position is the preset central position of the welding seam;
in the frequency domain, the partial derivative of the formula (1) relative to the weld filter h is calculated,
Figure FDA0002481837270000015
g、
Figure FDA0002481837270000016
and b0And filter h is represented as after FFT
Figure FDA0002481837270000017
G、
Figure FDA0002481837270000018
B0And H, the available initial weld filter frequency domain representation is:
Figure FDA0002481837270000019
wherein HlL ∈ {1, 2.., 28} denotes the l-th layer of the filter in the frequency domain, { denotes the complex conjugate;
A6. storing the obtained initial weld filter;
the on-line tracking stage is carried out according to the following steps:
B1. manually setting the coordinate p of the initial position of the weld center in the image0And let the initial search center ps＝p0；
B2. Acquiring an initial weld filter;
B3. collecting a detected image in real time;
B4. extracting the coordinate p in the detected imagesA rectangular area as a center is used as a search area;
B5. extracting the characteristics of the search area;
B6. calculating α the maximum response value of the filter and the feature of the search area1And α1Position coordinates p in the image1；
Simultaneously carrying out B7.1.1-B7.1.4 steps and B7.2.1-B7.2.4 steps:
b7.1.1 calculating p1And p0Pixel deviation of (2);
b7.1.2 pixel biasing into stack;
b7.1.3 determine if the stack is full? Calculating deviation control quantity, outputting the deviation control quantity to a motion control module, releasing a stack, and updating ps＝p0(ii) a If not, update ps＝p1；
B7.1.4 determine whether tracking is finished? NO, return to B3; if yes, the tracking is finished;
b7.2.1 in the coordinate p1Collecting background images of a welding seam area and two sides of the welding seam area for the center;
b7.2.2 extracting the characteristics of the weld region and the background image respectively;
b7.2.3 calculating the maximum response value α of the background image feature and the filter2；
B7.2.4 decision α1≥k×α2(ii) a If yes, updating the filter; no, determine if tracking is complete? Yes, tracking ends, no, return to B3.
2. The method for tracking a weld according to claim 1, wherein the step B7.1.3 of calculating deviation control values includes removing the maximum value and the minimum value of the deviation values, and averaging the remaining deviation values to obtain the motion deviation control values.
3. The method for seam tracking based on image matching according to claim 2, wherein said updating filter of step B7.2.4 is to modify the filter to be a weighted sum of the current calculated filter and the previous filter, namely:
Figure FDA0002481837270000021
wherein the content of the first and second substances,
Figure FDA0002481837270000022
representing the ith of the filter at the tth frame,
Figure FDA0002481837270000023
the l 1{ α th of the filter at the t-1 th frame1≥k×α2Is an indication function if α1≥k×α2True, then 1{ α1≥k×α2Equal to 1; otherwise, it equals 0.
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