A fillet weld recognition method for robot vision welding with resistance to reflection noise

Abstract

The present invention provides a fillet weld recognition method for robot vision welding that is resistant to reflective noise. The method comprises the following steps: setting a region of interest (ROI); calculating the grayscale value sum of each row of pixels within the ROI, and finding the row with the maximum cumulative sum; finding the row with the minimum cumulative grayscale value sum; finding the coarse coordinate point of the weld; calculating the two rows on either side of the weld with the maximum cumulative grayscale value sum; determining the starting position of each light bar; determining the center point using an internal boosting algorithm; and finally performing center fitting and weld recognition. The method can rapidly identify fillet welds.

Description

Technical Field

[0001] The present invention relates to the technical field of welding, and in particular to a fillet weld recognition method for robot vision welding that resists reflection noise. Background Art

[0002] Intelligent manufacturing technology is crucial to the development of the manufacturing industry. Welding technology is a key component of the manufacturing sector, and achieving automated, efficient, and intelligent welding is crucial to achieving intelligent manufacturing. Welding robots are increasingly being used in manufacturing. By adding visual recognition to weld positions, welding robots offer enhanced automation and intelligentization, a current research hotspot in intelligent manufacturing.

[0003] Fillet welds are a common type of weld in manufacturing. When made of highly reflective metals, images of these welds captured by line structured light vision sensors will contain strong reflective noise. This is particularly true for fillet welds made of bright metals such as stainless steel, aluminum alloy, and titanium alloy. However, many existing fillet weld recognition methods can only identify fillet welds without reflective noise, resulting in large center extraction errors, slow processing, and complex methods. Summary of the Invention

[0004] The present invention provides a fillet weld recognition method for robot vision welding that is resistant to reflective noise. A line structured light vision sensor composed of a line laser (or line structured light) and a camera is used to collect images of fillet welds illuminated by the line laser. The method then quickly processes the images and recognizes the welds.

[0005] This specification discloses a fillet weld recognition method for robot vision welding with anti-reflective noise, comprising:

[0006] Collect weld seam images through line structured light vision sensor;

[0007] Set a rectangular region of interest in the weld image. The minimum row and column coordinates of the rectangular region of interest are (x s ,y s );

[0008] Sum the grayscale values ​​of each row of pixels in the rectangular area of ​​interest and save the y value of each row i The corresponding pixel grayscale value accumulation sum i , i is the row number in the image, by comparing the grayscale values ​​of each row of pixels and sum i The size of , find the cumulative sum i The maximum value of sum max And the corresponding row position, which is recorded as y1. The row with the largest cumulative sum is the row position with the maximum cumulative sum of the reflected noise intensity and the light bar grayscale value;

[0009] The length interval [y1-row, y1+row] is set by the row position y1 of the maximum sum. The length interval must include the weld position. The value of row is generally one-fourth of the number of image rows. Compare sum within the length interval. i The size of , find the sum i Minimum sum min And the corresponding row position, which is the rough positioning row position of the fillet weld, is recorded as y2;

[0010] Set a 5×5 vertical template and search from left to right along row y2 and column 0 of the image to find the two boundary points of the light strip in the triangular area where the light strip and the reflection noise overlap. The triangular area is located at the fillet weld.

[0011] When the average grayscale value of the pixels in the template exceeds the set threshold of 200 for the first time, the left boundary of the weld position light bar is found; let the template continue to search along the y2 line to find the right boundary of the light bar. When the average grayscale value of the pixels in the template is less than the set threshold of 200 for the first time, the right boundary of the weld position light bar is found. The average value of the coordinates of the left and right boundary points is the rough coordinate of the weld position (x0, y2);

[0012] By sum i Construct a histogram with the weld as sum i Minimum value, sum on both sides of the weld i There is a local maximum for each, one of which is the global maximum sum max ; By comparing the size of y1 and y2, determine the sum on the other side of the search weld i local maximum sum max2 Search direction of the row, find sum max2 and the corresponding row position, which is denoted as y3;

[0013] Along y1 and y3, starting from (x0, y1) and (x0, y3), the light bar position is searched from left to right. In this process, a 5×5 horizontal template is set. When the average grayscale value of the pixels in the template is greater than the set threshold of 200 for the first time, the two light bar positions on both sides of the weld are found. The light bar centers P1 (x st , y1) and P2(x st , y3) points, these two points are the starting center points of the light strip segments on both sides of the weld;

[0014] Starting from the starting center points P1 and P2, the light bar is internally advanced to extract the center, and the light bar centers of the light bar segments on both sides of the weld are extracted respectively;

[0015] The center points of the two light strips are fitted using the least squares straight line fitting method to find the intersection of the two straight lines, which is the exact position of the fillet weld.

[0016] In this manual, sum i The formula is as follows:

[0017]

[0018] Among them, f(x i ,y j ) is the pixel (x i ,y j ) grayscale value, the minimum row and column coordinates are (x s ,y s ), large row and column coordinates (x n ,y m ).

[0019] In this specification, the methods for extracting the internal propulsion center of the light strip adopt positive (forward) internal propulsion and reverse (backward) internal propulsion respectively.

[0020] In this document, forward internal push center extraction and backward internal push center extraction are performed using starting centers P1 and P2, respectively. When performing forward internal push, the forward movement starts from the starting center and advances forward one pixel at a time; when performing backward internal push, the backward movement starts from the starting center and advances backward one pixel at a time. Forward push refers to moving along the positive y-axis of the image, while backward push refers to moving along the negative y-axis. When y1 is greater than y3, forward internal push center extraction starts at P1 and backward internal push center extraction starts at P2. When y1 is less than y3, forward internal push center extraction starts at P2 and backward internal push center extraction starts at P1.

[0021] In this manual, it is assumed that the current center point is (x t ,y t ), then the forward point (prediction point) is (x t ,y t +1), then the backward point (prediction point) is (x t ,y t -1); then, with the advancing point as the center, the grayscale values ​​and coordinates of w pixels to the left and right are counted, and the center is calculated using the grayscale centroid method. Let the coordinates of the advancing point (prediction point) be (x, y), then the grayscale value of the pixel is I(x, y), then the grayscale values ​​of w pixels to the left and right in the horizontal direction with (x, y) as the center are counted I(x+k, y). The grayscale value I(x+k, y) is the median of the grayscale values ​​of the pixels in the 3×3 window centered on the pixel point (x+k, y), f(x+k, y) is the grayscale value of the pixel (x+k, y), where k∈[-w, w]. The median formula is:

[0022]

[0023] In this specification, w is 2 / 3 of the light strip width w1. Since the width of the fillet weld light strip is uneven, wide at the two ends and thin at the weld, the light strip width changes linearly from the weld to the two ends of the light strip, and the weld width gradually increases. This linear change is expressed as:

[0024] w1=k b y+b;

[0025] y is the image row coordinate, k b is the linear variation slope of each line of light strip width, and b is the linear variation intercept. Since the weld width on both sides of the weld changes in opposite directions, the width of each line of light strip in the non-overlapping area of ​​the reflection noise and light strip on both sides of the weld is counted separately, and the k of the linear variation equation of different light strip widths on both sides of the weld is obtained by least squares fitting. b and b.

[0026] In this specification, the improved Niblack adaptive threshold algorithm calculates the center calculation threshold of the 2w pixel grayscale values, and the threshold is recorded as A. With the advancement point as the center, within the 2w pixel length, the pixels (x a ,y) and gray value I i (x a ,y), and calculate the light bar center of these pixels; I i (x a ,y) is the pixel (x a ,y) is the median of the grayscale values ​​of the pixels in the 3×3 window centered at ;

[0027] The central formula is:

[0028]

[0029] Among them, (U, y) is the new center position of the light bar.

[0030] In this specification, a 1×2w vertical window is set with the predicted center (x, y) of the light bar as the center, and then the average value and mean square error of the grayscale values ​​of the pixels in the window are calculated; u(x, y) is the average value and t(x, y) is the standard deviation, which are defined as:

[0031]

[0032] The improved Nibrak center threshold A calculation formula is as follows:

[0033] A=(Max+u(x,y)) / 2-0.2t(x,y);

[0034] Where Max is the maximum grayscale value in the window.

[0035] The embodiments of this specification can achieve at least the following beneficial effects:

[0036] The present invention proposes a weld recognition method based on row grayscale and statistical analysis, which can quickly identify fillet welds with reflective noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 Schematic diagram of the fillet weld recognition method for robot vision welding with resistance to reflective noise involved in the present invention.

[0039] Figure 2 It is a schematic diagram of the fillet weld reflection image involved in the present invention.

[0040] Figure 3 This is a schematic diagram of the accumulation and statistics of the grayscale values ​​of pixels in each row of an image involved in the present invention. DETAILED DESCRIPTION

[0041] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the embodiments of the present invention. Therefore, the drawings and description are to be regarded as illustrative in nature and not restrictive.

[0042] The disclosure below provides many different embodiments or examples for implementing different structures of the embodiments of the present invention. In order to simplify the disclosure of the embodiments of the present invention, the components and configurations of specific examples are described below. Of course, these are merely examples and are not intended to limit the embodiments of the present invention. In addition, the embodiments of the present invention may repeat reference numerals and / or reference letters in different examples. Such repetition is for the purpose of simplicity and clarity and does not in itself indicate the relationship between the various embodiments and / or configurations discussed.

[0043] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0044] like Figure 1 As shown, this embodiment provides a fillet weld recognition method for robot vision welding that is resistant to reflective noise, including:

[0045] Collect weld seam images through line structured light vision sensor;

[0046] According to prior knowledge, a rectangular region of interest (ROI) is set in the weld image, which should include the weld and the light strip.

[0047] Sum the grayscale values ​​of each row of pixels in the ROI area, and compare the cumulative sum of the grayscale values ​​of each row of pixels. i The size of , find the cumulative sum i The maximum value of sum max And the corresponding row position, which is recorded as y1. The row with the largest cumulative sum is the row position with the maximum cumulative sum of the reflected noise intensity and the light bar grayscale value;

[0048] The interval [y1-row,y1+row] is set by the row position y1 of the maximum sum found above. This interval should include the weld position. The value of row is generally one-fourth of the number of image rows. Compare sum in this interval. i The size of , find the sum i Minimum sum min And the corresponding row position, which is the rough positioning row position of the fillet weld, is recorded as y2.

[0049] Set up a 5×5 vertical template and search along row y2 and column 0 of the image from left to right to find the two boundary points of the triangular area where the light stripe and the reflected noise overlap. This area is located at the fillet weld. When the average grayscale value of the pixels in the template exceeds the set threshold of 200 for the first time, the left boundary of the light stripe at the weld location is found. After finding the left boundary point, continue searching along row y2 to find the right boundary of the light stripe. When the average grayscale value of the pixels in the template falls below the set threshold of 200 for the first time, the right boundary of the light stripe at the weld location is found. The average of the coordinates of the left and right boundary points is the rough weld location coordinate (x0, y2).

[0050] By sum i The constructed histogram shows that the weld is sum i Minimum value, sum on both sides of the weld i There is a local maximum for each, one of which is the global maximum sum max By comparing the size of y1 and y2, determine the sum on the other side of the search weld i local maximum sum max2 Search direction of the row, find sum max2 and the corresponding row position, which is denoted as y3;

[0051] The light bar locations are searched from left to right along y1 and y3, starting at (x0, y1) and (x0, y3). A 5×5 horizontal template is set during this process. When the average grayscale value of the pixels within the template exceeds the set threshold of 200 for the first time, the two light bar locations on either side of the weld are found. The light bar centers P1 and P2 at these two locations are calculated and become the starting centers of the light bar segments on either side of the weld.

[0052] Take P1(x st , y1) and P2(x st , y3) and begins internal center extraction of the light bar segments on both sides of the weld. The light bar center is extracted using two internal center extraction algorithms: forward internal push and reverse internal push. Forward push refers to pushing along the positive y-axis of the image, while backward push refers to pushing along the negative y-axis. When y1 is greater than y3, forward internal center extraction begins at P1 and backward internal center extraction begins at P2. When y1 is less than y3, forward internal center extraction begins at P2 and backward internal center extraction begins at P1.

[0053] The center points of the two light strips are fitted by the least squares straight line, and the intersection of the two straight lines is obtained. The intersection is the exact position of the fillet weld.

[0054] In a specific embodiment, the present invention provides a method for identifying fillet welds with anti-reflective noise based on line grayscale and statistical analysis of robot vision welding, which can quickly identify fillet welds with reflective noise. The reflective noise of fillet welds of highly reflective materials is as follows: Figure 2 As shown, the method includes:

[0055] Region of interest (ROI) setting:

[0056] In order to improve the calculation speed, a rectangular region of interest (ROI) is set in the weld image based on prior knowledge. The region should include the weld and the light strip. The ROI is defined as the following equation, where x s ,y s are the minimum row and column coordinates of ROI, x n ,y m are the maximum row and column coordinates of ROI respectively.

[0057]

[0058] Calculate the sum of the grayscale values ​​of each row of pixels in the ROI and find the row with the largest cumulative sum:

[0059] Sum the grayscale values ​​of each row of pixels in the ROI area, and compare the cumulative sum of the grayscale values ​​of each row of pixels. i The size of , find the cumulative sum i The maximum value of summax The row with the largest cumulative sum is y1, which is the row with the largest cumulative sum of the reflected noise intensity and the grayscale value of the light stripe. Based on experimental data from hundreds of images, this row is located a certain distance from the row where the weld is located, just away from the area with the most severe noise pollution.

[0060]

[0061] Among them, f(x i ,y j ) is the pixel (x i ,y j ) grayscale value, the minimum row and column coordinates are (x s ,y s ), large row and column coordinates (x n ,y m ).

[0062] Find the row with the smallest cumulative grayscale value:

[0063] In order to reduce the amount of calculation and accuracy, a certain length interval is set by the row position y1 of the maximum sum found above, and sum is compared within this interval. i The size of , find the sum i Minimum sum min And the corresponding row position, which is the approximate row position of the fillet weld, is recorded as y2. Figure 3 shown.

[0064] Find the coarse coordinate point of the weld:

[0065] Set up a 5×5 vertical template and search from left to right along row y2 and column 0 of the image for the two boundary points of the triangular region where the light stripe and the reflective noise overlap. This region is located at the fillet weld. When the average grayscale value of the pixels within the template exceeds the set threshold of 200 for the first time, the left boundary (x1, y2) of the weld light stripe is found. After finding the left boundary point, continue searching along row y2 for the right boundary of the light stripe. When the average grayscale value of the pixels within the template falls below the set threshold of 200 for the first time, the right boundary (x2, y2) of the weld light stripe is found. The average of the coordinates of the left and right boundary points is the rough weld position coordinate (x0, y2), where x0 = (x1 + x2) / 2.

[0066] Find the rows with the largest cumulative grayscale values ​​on both sides of the weld:

[0067] By sum i The constructed histogram shows that the weld is sum i Minimum value, sum on both sides of the weld iThere is a local maximum for each, one of which is the global maximum sum max By comparing the size of y1 and y2, determine the sum on the other side of the search weld i local maximum sum max2 Search direction of the row, find sum max2 and the corresponding row position, which is denoted as y3;

[0068] Find the starting position of the light bar separately:

[0069] Along y1 and y3, starting from (x0, y1) and (x0, y3), search for the position of the light bar from left to right. In this process, a 5×5 horizontal template is set. When the average grayscale value of the pixels in the template exceeds the set threshold of 200 for the first time, the two light bar positions on both sides of the weld are found. The light bar centers P1 (x st , y1) and P2(x st , y3) points, these two points are the starting center points of the light bar segments on both sides of the weld.

[0070] The inner boost algorithm is used to find the center point:

[0071] The forward and backward internal pushing center extraction algorithms are started with the starting center points P1 and P2 respectively. The forward and backward internal pushing center extraction methods have the same principle, but the pushing direction is different. Taking the forward internal pushing as an example, starting from the starting center, push forward one pixel (one row or one column) at a time; assuming the current center point is (x t ,y t ), then the forward point (prediction point) is (x t ,y t +1), then the backward point (prediction point) is (x t ,y t -1); then, with the advancing point as the center, the grayscale values ​​and coordinates of w pixels to the left and right are counted, and the center is calculated using the grayscale centroid method. Let the coordinates of the advancing point (prediction point) be (x, y), then the grayscale value of the advancing point pixel is I(x, y), then the grayscale values ​​of w pixels to the left and right in the horizontal direction with (x, y) as the center are counted I(x+k, y), the grayscale value I(x+k, y) is the median of the grayscale values ​​of the pixels in the 3×3 window centered on the pixel point (x+k, y), f(x+k, y) is the grayscale value of the pixel (x+k, y), where k∈[-w, w], and the median formula is:

[0072]

[0073] This step is to miniaturize and localize the median filter to improve computational efficiency and noise immunity. w is 2 / 3 of the light bar width w1. Since the width of the fillet weld light bar is uneven, wide at both ends and thin at the weld, the light bar width changes linearly from the weld to the ends, and the weld width gradually increases. This linear change can be expressed as:

[0074] w1=k b y+b;

[0075] y is the image row coordinate, k b is the linear variation slope of each line of light strip width, and b is the linear variation intercept. Since the weld width on both sides of the weld changes in opposite directions, the width of each line of light strip in the non-overlapping area of ​​the reflection noise and light strip on both sides of the weld is counted separately, and the k of the linear variation equation of different light strip widths on both sides of the weld is obtained by least squares fitting. b and b.

[0076] The improved Niblack adaptive threshold algorithm is used to calculate the central threshold of the grayscale value of the 2w pixels. The threshold is recorded as A. With the advancing point as the center, within the length of 2w pixels, the pixels (x a ,y) and gray value I i (x a ,y),I i (x a ,y) is the pixel (x a ,y) and calculate the median of the grayscale values ​​of the pixels in the 3×3 window centered at ,y) and calculate the center of the light stripe of these pixels; the center formula is:

[0077]

[0078] Among them, (U, y) is the new center position of the light bar.

[0079] The threshold (Nd) calculation formula of the traditional Nibrak method is as follows:

[0080] Nd=u(x,y)+Kt(x,y);

[0081] Where u(x,y) is the average grayscale value of a certain number of pixels in the local vicinity of the pixel point (x,y), t(x,y) is the local standard deviation of the pixel grayscale value, and K is an image-dependent coefficient that needs to be manually set based on the foreground and background of the image. K is usually set to -0.2 to 0.2.

[0082] To improve the robustness and accuracy of light stripe center extraction, more laser stripe details must be considered during center extraction, especially when there are uneven and weak light areas in the light stripe. Here, the traditional Nibrak method is improved to calculate the center extraction threshold, so that an appropriate threshold is used for calculating the center of each column of the light stripe. This method first sets a 1×2w vertical window centered on the predicted center (x, y) of the light stripe, and then calculates the mean and mean square error of the pixel grayscale values ​​within the window. u(x, y) is the mean and t(x, y) is the standard deviation, defined as:

[0083]

[0084]

[0085] The improved Nibrak center threshold A calculation formula is as follows:

[0086] A=(Max+u(x,y)) / 2-0.2t(x,y);

[0087] Where Max is the maximum grayscale value in the window.

[0088] Center fitting and weld identification algorithm:

[0089] Fit the center points of the two segments using the least squares straight line to find the intersection of the two straight lines, which is the exact location of the fillet weld.

[0090] The above embodiments are intended to illustrate the present invention, not to limit the present invention. Therefore, changes in illustrative values ​​or substitutions of equivalent components should still fall within the scope of the present invention.

[0091] From the above detailed description, it will be clear to those skilled in the art that the present invention can indeed achieve the aforementioned objectives and is in compliance with the provisions of the Patent Law.

[0092] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as covering the preferred embodiments and all changes and modifications that fall within the scope of the invention. The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

[0093] It should be noted that the above description of the relevant processes is for illustration and purpose only and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the processes under the guidance of this specification. However, such modifications and changes are still within the scope of this specification.

[0094] The basic concepts have been described above. It will be apparent to those skilled in the art after reading this application that the above disclosures are merely illustrative and do not constitute limitations on this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to this application. Such modifications, improvements, and amendments are suggested in this application and remain within the spirit and scope of the exemplary embodiments of this application.

[0095] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or more in different places in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0096] In addition, it will be understood by those skilled in the art that various aspects of the present application can be illustrated and described by a number of patentable categories or situations, including any new and useful combination of processes, machines, products or substances, or any new and useful improvements thereto. Therefore, various aspects of the present application can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software can all be referred to as "units", "modules" or "systems". In addition, various aspects of the present application can take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0097] The computer program code required for the operation of each part of the application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, conventional procedural programming languages ​​such as C programming language, VisualBasic, Fortran2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy or other programming languages. The program code can be run completely on the user's computer, or run on the user's computer as an independent software package, or run partly on the user's computer and partly on a remote computer, or run completely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or be connected to an external computer (such as by the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0098] In addition, unless expressly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some embodiments of the invention that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a pure software solution, for example, installation on an existing server or mobile device.

[0099] Similarly, it should be noted that in order to simplify the presentation of this disclosure and thereby facilitate understanding of one or more of the invention's embodiments, the foregoing descriptions of the embodiments of this disclosure sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this approach should not be interpreted as reflecting an intention that the claimed subject matter requires more features than expressly recited in each claim. Rather, the subject matter of the invention may possess fewer features than the single embodiment described above.

Claims

1. A fillet weld recognition method for robot vision welding with anti-reflective noise, characterized in that: include: Collect weld seam images through line structured light vision sensor; Set a rectangular region of interest in the weld image. The minimum row and column coordinates of the rectangular region of interest are (x s ,y s ); Sum the grayscale values ​​of each row of pixels in the rectangular area of ​​interest and save the y value of each row i The corresponding pixel grayscale value accumulation sum i , by comparing the cumulative sum of the grayscale values ​​of each row of pixels i The size of , find the cumulative sum i The maximum value of sum max And the corresponding row position, which is recorded as y1. The row with the largest cumulative sum is the row position with the maximum cumulative sum of the reflected noise intensity and the light bar grayscale value; Set the length interval [y1-row,y1+row] by the row position y1 of the maximum sum. This length interval should include the weld position. The value of row is one-fourth of the number of image rows. Compare sum within the length interval. i The size of , find the sum i Minimum sum min And the corresponding row position, which is the rough positioning row position of the fillet weld, is recorded as y2; Set a 5×5 vertical template and search from left to right along row y2 and column 0 of the image to find the two boundary points of the light strip in the triangular area where the light strip and the reflection noise overlap. The triangular area is located at the fillet weld. When the average grayscale value of the pixels in the template exceeds the set threshold of 200 for the first time, the left boundary of the weld position light bar is found; let the template continue to search along the y2 line to find the right boundary of the light bar. When the average grayscale value of the pixels in the template is less than the set threshold of 200 for the first time, the right boundary of the weld position light bar is found. The average value of the coordinates of the left and right boundary points is the rough coordinate of the weld position (x0, y2); By sum i Construct a histogram with the sum at the weld i Minimum value, sum on both sides of the weld i There is a local maximum for each, one of which is the global maximum sum max ; By comparing the size of y1 and y2, determine the sum on the other side of the search weld i local maximum sum max2 Search direction of the row, find sum max2 And the corresponding row position coordinates, the row is recorded as y3; Along y1 and y3, starting from (x0, y1) and (x0, y3), the light bar position is searched from left to right. In this process, a 5×5 horizontal template is set. When the average grayscale value of the template pixels exceeds the set threshold of 200 for the first time, the two light bar positions on both sides of the weld are found. The light bar centers P1 (x st , y1) and P2(x st , y3) points, these two points are the starting center points of the light strip segments on both sides of the weld; Starting from the starting center points P1 and P2, the light bar is internally advanced to extract the center, and the light bar centers of the light bar segments on both sides of the weld are extracted respectively; The center points of the two light strips are fitted using the least squares straight line fitting method to find the intersection of the two straight lines, which is the exact position of the fillet weld.

2. The method for identifying fillet welds with robot vision welding and anti-reflective noise according to claim 1 is characterized in that: sum i The formula is as follows: Among them, f(x i ,y j ) is the pixel (x i ,y j ) grayscale value, the minimum row and column coordinates are (x s ,y s ), maximum row and column coordinates (x n ,y m ).

3. The fillet weld recognition method for robot vision welding with anti-reflective noise according to claim 1 is characterized in that: The methods for extracting the internal pushing center of the light strip are forward pushing and backward internal pushing.

4. The method for identifying fillet welds with robot vision welding and anti-reflective noise according to claim 3 is characterized in that: The forward internal pushing center extraction and the backward internal pushing center extraction are performed with P1 and P2 as the starting center points respectively; when pushing forward internally, the center is pushed forward starting from the starting center, and only one pixel is pushed forward at a time; when pushing backward internally, the center is pushed backward starting from the starting center, and only one pixel is pushed forward at a time; pushing forward means pushing along the positive direction of the image y-axis, and pushing backward means pushing along the negative direction of the image y-axis; when y1 is greater than y3, the forward internal pushing center extraction is started from P1, and the backward internal pushing center extraction is started from P2; when y1 is less than y3, the forward internal pushing center extraction is started from P2, and the backward internal pushing center extraction is started from P1.

5. The method for identifying fillet welds with robot vision welding and anti-reflective noise according to claim 4 is characterized in that: Assume that the current center point is (x t ,y t ), then the forward point, that is, the predicted point, is (x t ,y t +1), then the backward point, that is, the predicted point, is (x t ,y t -1); then, with the advancing point as the center, the grayscale values ​​and coordinates of w pixels are counted to the left and right respectively, and the center is calculated using the grayscale centroid method; let the coordinates of the advancing point, i.e. the prediction point, be (x, y), then the grayscale value of the pixel is I(x, y), then these grayscale values ​​of w pixels are counted to the left and right in the horizontal direction with (x, y) as the center, and the grayscale value I(x+k, y) is the median of the grayscale values ​​of the pixels in the 3×3 window centered on the pixel point (x+k, y), f(x+k, y) is the grayscale value of the pixel (x+k, y), where k∈[-w, w], and the median formula is:

6. The method for identifying fillet welds with robot vision welding and anti-reflective noise according to claim 5, characterized in that: w is 2 / 3 of the light strip width w1. Since the width of the fillet weld light strip is uneven, wide at both ends and thin at the weld, the light strip width changes linearly from the weld to the ends, and the weld width gradually increases. This linear change is expressed as: w1=k b y+b; y is the image row coordinate, k b is the linear variation slope of each line of light strip width, and b is the linear variation intercept. Since the weld width on both sides of the weld changes in opposite directions, the width of each line of light strip in the non-overlapping area of ​​the reflection noise and light strip on both sides of the weld is counted separately, and the k of the linear variation equation of different light strip widths on both sides of the weld is obtained by least squares fitting. b and b.

7. The method for identifying fillet welds with robot vision welding and anti-reflective noise according to claim 6, characterized in that: The improved Niblack adaptive threshold algorithm is used to calculate the central threshold of the grayscale value of the 2w pixels. The threshold is recorded as A. With the advancing point as the center, within the length of 2w pixels, the pixels (x a ,y) and gray value I i (x a ,y), and calculate the light bar center of these pixels; I i (x a ,y) is the pixel (x a ,y) is the median of the grayscale values ​​of the pixels in the 3×3 window centered at ; the center formula is: Among them, (U, y) is the new center position of the light bar.

8. The method for identifying fillet welds with robot vision welding and anti-reflective noise according to claim 7, characterized in that: A 1×2w vertical window is set with the predicted center (x, y) of the light bar as the center, and then the mean and mean square error of the grayscale values ​​of the pixels in the window are calculated; u(x, y) is the mean and t(x, y) is the standard deviation, which are defined as: The improved Nibrak center threshold A calculation formula is as follows: A=(Max+u(x,y)) / 2-0.2t(x,y); Where Max is the maximum grayscale value in the window.

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