CN118570072A - Burr detection method and system in titanium metal processing process - Google Patents
Burr detection method and system in titanium metal processing process Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 62
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 title claims abstract description 56
- 239000010936 titanium Substances 0.000 title claims abstract description 56
- 229910052719 titanium Inorganic materials 0.000 title claims abstract description 56
- 229910052751 metal Inorganic materials 0.000 title claims abstract description 53
- 239000002184 metal Substances 0.000 title claims abstract description 53
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- 238000001914 filtration Methods 0.000 claims description 15
- 238000003754 machining Methods 0.000 claims description 12
- 238000013507 mapping Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 8
- 238000005555 metalworking Methods 0.000 claims 1
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- 238000004458 analytical method Methods 0.000 description 18
- 238000005520 cutting process Methods 0.000 description 8
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Abstract
The invention relates to the field of image analysis, in particular to a burr detection method and system in a titanium metal processing process. According to the method, firstly, a gray level image of a processed titanium metal surface to be detected is obtained, edge detection is carried out, a processed edge line, a suspected burr edge line and a texture edge line in the gray level image are obtained, the possibility of burrs of the suspected burr edge line is obtained according to the gray level change of each pixel point on the suspected burr edge line and the gray level change difference of each pixel point on the processed edge line, gradient values of each pixel point on a reference texture edge line of the suspected burr edge line are analyzed, burr blurring factors of the suspected burr edge line are obtained, and then each suspected burr edge line is filtered based on the obtained noise influencing factors, an enhanced image is obtained, and edge detection is carried out on the enhanced image, so that burr positions in the enhanced image are obtained. The invention eliminates the interference of noise on burr detection and improves the detection precision of burrs on the surface of the titanium metal processing.
Description
Technical Field
The invention relates to the field of image analysis, in particular to a burr detection method and system in a titanium metal processing process.
Background
In the titanium metal cutting process, due to the action of the propelling force of the cutter, part of materials are extruded and stretched to form more outstanding burrs, the difficulty and cost of subsequent processing procedures can be increased due to the existence of the burrs, and the performance of titanium metal products is affected, so that the burrs in the titanium metal processing process are required to be detected, and the quality of the titanium metal products is ensured.
In the related art, a machine vision detection technology is generally used for detecting the burr part of the titanium metal processing surface image, but the noise in the image cannot be effectively removed due to the fact that the gray value of the pixel point is changed due to the existence of noise in the image, and therefore the noise and the burr cannot be accurately distinguished by the existing method, and the detection precision of the burr on the titanium metal processing surface is reduced.
Disclosure of Invention
In order to solve the technical problems that the existing method cannot accurately distinguish noise from burrs, so that the noise in an image cannot be effectively removed, and the detection precision of burrs on the surface of the titanium metal processing is reduced, the invention aims to provide a burr detection method and system in the titanium metal processing process, and the adopted technical scheme is as follows:
the invention provides a burr detection method in a titanium metal processing process, which comprises the following steps:
acquiring a gray level image of the surface of the titanium metal to be processed, and performing edge detection on the gray level image to obtain a processed edge line, a suspected burr edge line and a texture edge line in the gray level image;
Obtaining a gray scale change factor of each pixel point according to the distribution of gray scale values of each pixel point in a preset neighborhood taking each pixel point as a center; taking any suspected burr edge line as a target edge line, taking two processing edge lines adjacent to the target edge line as reference processing edge lines of the target edge line, and obtaining the burr possibility of the target edge line according to the gray scale change factors of all pixel points on the target edge line and the difference of the gray scale change factors of the pixel points between the target edge line and the reference processing edge line;
Acquiring the mass centers of the target edge line and each texture edge line, and taking the distance between the target edge line and each texture edge line as the distance parameter of each texture edge line; selecting a preset number of texture edge lines with minimum distance parameters as reference texture edge lines of target edge lines; obtaining a burr blurring factor of the target edge line according to the gradient value of each pixel point on each reference texture edge line and the distance parameter of each reference texture edge line;
obtaining a noise influence factor of the target edge line according to the burr possibility of the target edge line and the difference of the burr blurring factors; filtering the gray level image based on the noise influence factors of each suspected burr edge line to obtain an enhanced image;
And performing edge detection on the enhanced image to obtain a burr part in the enhanced image.
Further, the obtaining the processing edge line, the suspected burr edge line and the texture edge line in the gray image includes:
Performing edge detection on the gray level image based on an edge detection algorithm to obtain a processing edge line and a texture edge line in the gray level image;
And comparing the processing edge line with a preset processing track, and taking the part of the processing edge line, which is missing relative to the preset processing track, as a suspected burr edge line.
Further, the obtaining the gray scale variation factor of each pixel point includes:
Taking any pixel point in the gray level image as a target pixel point, and taking any pixel point in a preset adjacent area taking the target pixel point as a center as a pixel point to be measured; if the gray value of the pixel to be detected is greater than or equal to the gray value of all the pixels in a preset window taking the pixel to be detected as the center, marking the pixel to be detected as a maximum pixel, and acquiring all the maximum pixels in a preset adjacent area;
taking any one maximum pixel point as a target maximum pixel point, and taking other maximum pixel points closest to the target maximum pixel point as reference maximum pixel points of the target maximum pixel point;
taking the average value of the gray values of the target maximum pixel point and the corresponding reference maximum pixel point as a first gray parameter of the target maximum pixel point; analyzing the integral level of gray values of all pixel points on the connecting line between the target maximum pixel point and the corresponding reference maximum pixel point to obtain a second gray parameter of the target maximum pixel point; acquiring an initial gray scale variation parameter of a target maximum pixel point according to the first gray scale parameter and the second gray scale parameter, wherein the initial gray scale variation parameter is positively correlated with the first gray scale parameter, and the initial gray scale variation parameter is negatively correlated with the second gray scale parameter;
Performing negative correlation mapping on the distance between the target maximum pixel point and the corresponding reference maximum pixel point to obtain a first distance weight of the target maximum pixel point, and weighting the initial gray level change parameter by using the first distance weight of the target maximum pixel point to obtain a real gray level change parameter of the target maximum pixel point;
analyzing the overall level of the real gray scale change parameters of all the maximum pixel points in the preset neighborhood, and carrying out normalization processing to obtain the gray scale change factor of the target pixel point.
Further, the obtaining the burr likelihood of the target edge line includes:
analyzing the overall level of the gray scale change factors of all pixel points on the target edge line or each reference processing edge line to obtain the overall gray scale change factors of the target edge line or each reference processing edge line;
According to the difference of the integral gray scale change factors between the target edge line and each reference processing edge line, gray scale change difference values of each reference processing edge line are obtained, and the maximum value of the gray scale change difference values of all the reference processing edge lines is used as a gray scale change characteristic value of the target edge line;
and obtaining the burr possibility of the target edge line according to the integral gray scale change factor and the gray scale change characteristic value of the target edge line, wherein the burr possibility is positively correlated with the gray scale change characteristic value of the target edge line, the burr possibility is negatively correlated with the integral gray scale change factor of the target edge line, and the burr possibility is a numerical value after normalization processing.
Further, the obtaining the burr blurring factor of the target edge line includes:
Analyzing the overall level of the gradient values of all pixel points on each texture edge line to obtain a gradient distribution value of each texture edge line, and analyzing the overall level of the gradient distribution value of all texture edge lines to obtain an overall gradient distribution parameter;
Obtaining an initial gradient characteristic value of each reference texture edge line according to the overall gradient distribution parameters and the gradient distribution value of each reference texture edge line; the initial gradient characteristic value is positively correlated with the overall gradient distribution parameter, and the initial gradient characteristic value is negatively correlated with the gradient distribution value of each reference texture edge line;
performing negative correlation mapping on the distance parameters of each reference texture edge line to obtain a second distance weight of each reference texture edge line; weighting the initial gradient characteristic value of each reference texture edge line by using the second distance weight of each reference texture edge line to obtain a real gradient characteristic value of each reference texture edge line;
analyzing the overall level of the real gradient characteristic values of all the reference texture edge lines, and carrying out normalization processing to obtain the burr fuzzy factor of the target edge line.
Further, the obtaining the noise impact factor of the target edge line includes:
And carrying out normalization processing on the difference of the burr possibility and the burr blurring factor according to the target edge line to obtain a noise influence factor of the target edge line.
Further, the obtaining the enhanced image includes:
And taking the noise influence factor of each suspected burr edge line as a scale factor used by a wiener filtering algorithm, and filtering the area where the minimum circumcircle of each suspected burr edge line in the gray level image is located based on the wiener filtering algorithm to obtain an enhanced image.
Further, the performing edge detection on the enhanced image to obtain the burr part in the enhanced image includes:
performing edge detection on the enhanced image to obtain an optimized processing edge line in the enhanced image;
Comparing the optimized machining edge line with a preset machining track, and taking the missing part of the optimized machining edge line relative to the preset machining track as a burr part in the enhanced image.
Further, the preset number is an integer ranging from 10 to 30.
The invention also provides a burr detection system in the titanium metal processing process, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of any one of the burr detection methods in the titanium metal processing process when executing the computer program.
The invention has the following beneficial effects:
The invention considers that the noise in the image can cause the change of the gray value of the pixel point, so that the existing method can not accurately distinguish the noise from the burr, thereby being incapable of effectively removing the noise in the image and reducing the detection precision of the burr on the titanium metal processing surface, therefore, the invention firstly obtains the gray image of the titanium metal surface to be processed, carries out edge detection on the gray image, thereby obtaining the processing edge line, the suspected burr edge line and the texture edge line in the gray image, and considers that the noise and the burr existing in the gray image can cause the change of the gray value of the pixel point in the local area, thus reflecting the change characteristic of the local gray value of each pixel point through the gray change factor, providing a data basis for the subsequent distinction of the noise and the burr, and providing the suspected burr edge line formed by the noise, on a suspected burr edge line formed by burrs, the gray scale change factor of the pixel point is smaller, and the difference between the gray scale change factor of the pixel point and the gray scale change factor of the pixel point on a reference processing edge line is larger, so that the possibility of burrs on a target edge line can be reflected through the possibility of burrs, the visual effect of the surrounding area is blurred due to the fact that the contrast of the surrounding area can be reduced by illumination, the gradient value of the pixel point on the texture edge line of the surrounding area is reduced, the gradient value of the pixel point on the texture edge line of the surrounding area of the suspected burr edge line formed by noise does not change greatly, the possibility that the target edge line is affected by burrs instead of noise can be reflected through the acquired burr blurring factor, and meanwhile, the possibility of burrs on the target edge line and the burr blurring factor can be greatly different due to the existence of noise, therefore, the degree of noise influence on the target edge line can be reflected through the acquired noise influence factors, the gray level image is filtered, interference of noise in the gray level image on burr detection is eliminated, and the detection precision of the burrs on the titanium metal processing surface is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for detecting burrs during a titanium metal processing process according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a distribution of processed edge lines, suspected burr edge lines, and textured edge lines according to one embodiment of the present invention;
FIG. 3 is a flowchart of a method for obtaining a burr probability of a target edge line according to an embodiment of the present invention;
Fig. 4 is a flowchart of a method for obtaining a burr blur factor of a target edge line according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description refers to specific implementation, structure, characteristics and effects of the method and the system for detecting burrs in the titanium metal processing process according to the invention by combining the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a specific scheme of a burr detection method and a system in a titanium metal processing process, which are specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of a burr detection method in a titanium metal processing process according to an embodiment of the invention is shown, the method includes:
Step S1: and acquiring a gray level image of the surface of the processed titanium metal to be detected, and performing edge detection on the gray level image to obtain a processed edge line, a suspected burr edge line and a texture edge line in the gray level image.
In the titanium metal cutting process, due to the thrust action of a cutter, part of materials are extruded and stretched to form more outstanding burrs, the existence of the burrs can increase the difficulty and cost of subsequent processing procedures and influence the performance of titanium metal products, so that the burrs in the titanium metal processing process are required to be detected to ensure the quality of the titanium metal products, in the related art, the burrs of an image on the surface of the titanium metal processing are usually detected by using a machine vision detection technology such as edge detection or threshold segmentation, but the noise in the image can cause the change of gray values of pixels, so that the noise and the burrs cannot be accurately distinguished by the traditional method, the noise in the image cannot be effectively removed, and the detection precision of the burrs on the surface of the titanium metal processing is reduced.
According to the embodiment of the invention, an industrial camera is used for collecting an original image of the surface of the processed titanium metal to be detected, and the original image is a multi-channel image generally, so that the complexity of subsequent calculation is increased, and in order to reduce the calculation amount of subsequent image processing and improve the processing speed, the collected original image is subjected to gray processing in one embodiment of the invention and is converted into a single-channel gray image. It should be noted that the graying process is a technical means well known to those skilled in the art, and will not be described herein.
Because the cutting edge features of the gray image at the positions of the burrs are covered due to the fact that the burrs are rough at certain local positions on the edges formed after the cutting of the titanium metal, that is to say, the cutting edge features of the gray image at the positions of the burrs are not particularly obvious, the gray image can be subjected to edge detection based on the features, so that the processed edge lines and the suspected burr edge lines in the gray image are obtained, and meanwhile, texture features on the titanium metal surface are formed, the texture edge lines on the surface of the titanium metal can be obtained after the edge detection, the suspected burr edge lines, the processed edge lines and the texture edge lines can be further analyzed, noise in the gray image can be effectively removed, the detection precision of the burrs is improved, and the fact that the noise existing in the image can cover the edge features after the cutting is needed is explained that the formation factors of the suspected burr edge lines are two kinds, namely the suspected burr edge lines formed by the burrs and the suspected burr edge lines formed by the noise.
Preferably, in one embodiment of the present invention, the method for acquiring the processing edge line, the suspected burr edge line and the texture edge line in the gray-scale image specifically includes:
Based on an edge detection algorithm, such as a Canny edge detection algorithm or other edge detection methods, edge detection is performed on a gray level image to obtain a processed edge line and a texture edge line in the gray level image, and due to burrs and noise existing in the gray level image, edge features formed in a cutting process are covered, so that edges formed in the cutting process are not continuous, that is, a plurality of processed edge lines can be detected through the edge detection algorithm, and a break-off missing phenomenon existing between the processed edge lines is caused by the burrs and the noise, so that the processed edge lines and a preset processing track can be compared, a part of the processed edge line, which is missing relative to the preset processing track, is used as a suspected burr edge line, wherein the preset processing track is a known processing track designed before processing of titanium metal, the edge line formed after processing of titanium metal is complete under the ideal condition that the burrs and the noise do not exist, and is identical to the preset processing track, and fig. 2 shows that the distribution of the processed edge line, the texture edge line, the edge line and the texture edge line and the edge line exist between the processed edge line and the edge line are shown in the embodiment of the invention, wherein the distribution line F-7 is the suspected burr line, and the suspected burr line is the edge line is shown as the line F-7.
Step S2: obtaining a gray scale change factor of each pixel point according to the distribution of gray scale values of each pixel point in a preset neighborhood taking each pixel point as a center; and taking any suspected burr edge line as a target edge line, taking two processing edge lines adjacent to the target edge line as reference processing edge lines of the target edge line, and obtaining the burr possibility of the target edge line according to the gray scale change factors of all pixel points on the target edge line and the difference of the gray scale change factors of the pixel points between the target edge line and the reference processing edge line.
From the above analysis, two types of suspicious burr edge lines exist, one is the suspicious burr edge line formed by burrs, and the other is the suspicious burr edge line formed by noises, so that the noises can influence the accurate detection of the burrs, the randomness of the noises is stronger, the degree of change of the gray value of the local pixel point caused by the noises is larger relative to the burrs, the difference exists between the local distribution characteristics of the gray value of the pixel point on the two suspicious burr edge lines, and the local distribution characteristics of the gray value of the pixel point between the two suspicious burr edge lines and the processing edge line are also different, therefore, the distribution situation of the gray value of each pixel point in the preset neighborhood with each pixel point as the center can be analyzed firstly, the local gray change characteristics of each pixel point are reflected by the acquired gray change factor, the data basis is provided for the follow-up distinguishing of the noises and the burrs, wherein the size of the preset neighborhood is generally an odd number of more than 3, the preset neighborhood is set to be 5 in one embodiment of the invention, namely the preset neighborhood is oneThe specific size of the preset neighborhood may also be set by the practitioner according to the specific implementation scenario, which is not limited herein.
Preferably, in one embodiment of the present invention, the method for acquiring the gray scale variation factor of each pixel specifically includes:
Firstly, taking any pixel point in a gray level image as a target pixel point, and taking any pixel point in a preset adjacent area taking the target pixel point as a center as a pixel point to be tested; if the gray value of the pixel to be detected is greater than or equal to the gray value of all the pixels in a preset window taking the pixel to be detected as the center, marking the pixel to be detected as a maximum pixel, and obtaining all the maximum pixels in a preset neighborhood, wherein the size of the preset window needs to be smaller than the size of the preset neighborhood, in one embodiment of the invention, the size of the preset window is set to be 3, and the specific size of the preset window can also be set by an implementer according to specific implementation scenes, so that the method is not limited.
It should be noted that, for the pixel points at the boundary of the preset neighborhood, the preset window cannot be established with the pixel points as the center, at this time, the preset neighborhood may be subjected to boundary expansion, for example, in one embodiment of the present invention, the size of the preset neighborhood is 5, the size of the preset window is 3, at this time, the size of the preset neighborhood may be expanded to 7, where the expanded pixel points do not belong to the preset neighborhood, and only the preset window can be conveniently established with the pixel points at the boundary of the preset neighborhood as the center, so that the analysis can be conveniently and smoothly performed.
Then, taking any one maximum pixel point as a target maximum pixel point, and taking other maximum pixel points closest to the target maximum pixel point as reference maximum pixel points of the target maximum pixel point; taking the average value of the gray values of the target maximum pixel point and the corresponding reference maximum pixel point as a first gray parameter of the target maximum pixel point; the method comprises the steps of analyzing the overall level of gray values of all pixel points on a connecting line between a target maximum pixel point and a corresponding reference maximum pixel point to obtain a second gray parameter of the target maximum pixel point, wherein the larger the first gray parameter of the target maximum pixel point relative to the second gray parameter is, the larger the change degree of the gray value of the pixel point between the target maximum pixel point and the reference maximum pixel point is, so that the initial gray change parameter of the target maximum pixel point can be obtained according to the first gray parameter and the second gray parameter, the initial gray change parameter is positively correlated with the first gray parameter, the initial gray change parameter is negatively correlated with the second gray parameter, and the larger the initial gray change parameter is, the larger the change degree of the gray value of the pixel point between the target maximum pixel point and the reference maximum pixel point is.
In the embodiment of the invention, the average value or the median of the gray values of all the pixel points on the connecting line between the target maximum pixel point and the corresponding reference maximum pixel point can be used as the second gray parameter of the target maximum pixel point, so that the analysis of the whole level of the gray values of all the pixel points on the connecting line between the target maximum pixel point and the corresponding reference maximum pixel point is realized, and the analysis is not limited herein.
In one embodiment of the invention, the first gray scale parameter of the target maximum pixel point can be used as a numerator, the second gray scale parameter of the target maximum pixel point can be used as a denominator, and the ratio can be used as the initial gray scale variation parameter of the target maximum pixel point.
Finally, considering that the closer the distance between the target maximum pixel point and the corresponding reference maximum pixel point is, the faster the gray value of the pixel point between the target maximum pixel point and the corresponding reference maximum pixel point changes, so that the distance between the target maximum pixel point and the corresponding reference maximum pixel point can be subjected to negative correlation mapping, a first distance weight of the target maximum pixel point is obtained, the first distance weight of the target maximum pixel point is utilized to weight an initial gray change parameter, a real gray change parameter of the target maximum pixel point is obtained, the real gray change parameter of each maximum pixel point in a preset adjacent area can be obtained through the same method, the larger the real gray change parameter is, the stronger the gray value of the pixel point between the maximum pixel point and the reference maximum pixel point is, further the integral level of the real gray change parameter of all the maximum pixel points in the preset adjacent area is analyzed, the normalization processing is carried out, the gray change factor of the target pixel point is obtained, and the degree of the larger gray change factor of the gray value in the preset adjacent area of the target pixel point is described.
In the embodiment of the invention, the negative correlation mapping can be performed by using a negative correlation function such as an inverse proportion function, and the negative correlation mapping is not limited herein.
In the embodiment of the invention, the average value or the median of the real gray scale variation parameters of all the maximum pixel points in the preset neighborhood can be used as the gray scale variation factor of the target pixel point to realize the analysis of the whole level of the real gray scale variation parameters of all the maximum pixel points in the preset neighborhood, and the analysis is not limited herein.
In one embodiment of the present invention, the normalization process may specifically be, for example, maximum and minimum normalization processes, and the normalization in the subsequent steps may be performed by using the maximum and minimum normalization processes, and in other embodiments of the present invention, other normalization methods may be selected according to a specific range of values, which will not be described herein.
The expression of the gray scale variation factor of the target pixel point may specifically be, for example:
Wherein, A gray scale variation factor representing a target pixel; Representing the first in a preset neighborhood centered on the target pixel point A first gray scale parameter for each largest pixel; Representing the first in a preset neighborhood centered on the target pixel point A second gray scale parameter for each of the largest pixels; Representing the first in a preset neighborhood centered on the target pixel point Initial gray scale variation parameters of the maximum pixel points; representing the first in the preset neighborhood The distance between each maximum pixel point and the corresponding reference maximum pixel point can be calculated by using the Euclidean distance; Representing the first in a preset neighborhood centered on the target pixel point A first distance weight for a largest pixel point; Representing the first in a preset neighborhood centered on the target pixel point Real gray scale variation parameters of the maximum pixel points; Representing the number of the maximum pixel points in the preset neighborhood taking the target pixel point as the center; Representing the normalization function.
The gray scale change factor of each pixel point in the gray scale image can be obtained by the same method.
As can be seen from the above analysis, compared with the burr, the randomness of the noise is stronger, the change degree of the gray value of the local pixel point caused by the noise is larger, so that the difference exists between the local distribution characteristics of the gray values of the pixel points on the two suspected burr edge lines, and meanwhile, the gray value distribution of the local areas of the pixel points on the processing edge lines is more uniform and smooth, so that the local distribution characteristics of the gray values of the pixel points between the two suspected burr edge lines and the processing edge lines are also different, and the specific expression is as follows: compared with the suspected burr edge lines formed by noise, the degree of local gray value change of pixel points on the suspected burr edge lines formed by the burrs is small, and the difference of gray value change conditions of pixel points on the processed edge lines is large, so that in the follow-up process, for any suspected burr edge line, any suspected burr edge line is taken as a target edge line, two processed edge lines adjacent to the target edge line are taken as reference processed edge lines of the target edge line, the follow-up comparison analysis is facilitated, referring to fig. 2, assuming that the suspected burr edge line a is the target edge line, the processed edge lines A and F are reference processed edge lines of the suspected burr edge line a, gray change factors of all pixel points on the target edge line and gray change factors of the pixel points between the target edge line and the reference processed edge line are analyzed, the probability that the target edge line is formed by the burrs is reflected by the obtained burrs is high, the probability that the target edge line is more likely to exist, the follow-up process is based on the probability that the burrs exist on the target edge line, and the further analysis is further based on the probability that the titanium burrs exist in the processed image, and the further accuracy of the titanium noise is improved.
Preferably, in one embodiment of the present invention, the method for acquiring the burr possibility of the target edge line specifically includes:
referring to fig. 3, a flowchart of a method for acquiring a possibility of burrs of a target edge line according to an embodiment of the invention is shown.
Step S201: and respectively analyzing the overall level of the gray scale change factors of all pixel points on the target edge line and each reference processing edge line to obtain the overall gray scale change factors of the target edge line and each reference processing edge line.
From the above analysis, it is known that, with respect to the suspected burr edge line formed by noise, the degree of local gray value change of the pixel point on the suspected burr edge line formed by the burr is smaller, and the difference from the local gray value change condition of the pixel point on the processing edge line is larger, so that the overall level of the gray change factors of all the pixel points on the target edge line and each reference processing edge line can be analyzed first to obtain the overall gray change factor of the target edge line and each reference processing edge line, and the greater the overall gray change factor of the target edge line or each reference processing edge line, the greater the degree of local gray change of the pixel point on the target edge line or each reference processing edge line is indicated, so that a data basis is provided for the possibility of burr of the target edge line for subsequent calculation and analysis.
In the embodiment of the present invention, an average value or a median of the gray scale variation factors of all the pixel points on the target edge line or each reference processing edge line may be used as the overall gray scale variation factor of the target edge line or each reference processing edge line, so as to implement analysis of the overall level of the gray scale variation factors of all the pixel points on the target edge line or each reference processing edge line, which is not limited herein, specifically: taking the average value or the median of the gray scale change factors of all the pixel points on the target edge line as the overall gray scale change factor of the target edge line, and taking the average value or the median of the gray scale change factors of all the pixel points on each reference processing edge line as the overall gray scale change factor of each reference processing edge line.
Step S202: and obtaining a gray level change difference value of each reference processing edge line according to the difference of the integral gray level change factors between the target edge line and each reference processing edge line, and taking the maximum value of the gray level change difference values of all the reference processing edge lines as a gray level change characteristic value of the target edge line.
The pixel points on the suspected burr edge line formed by the burrs have large differences from the local gray value change condition of the pixel points on the processing edge line, and the method is specifically expressed as follows: the difference of the integral gray scale change factors between the suspected burr edge line and the processing edge line formed by the burrs is larger, so that the gray scale change difference value of each reference processing edge line can be obtained according to the difference of the integral gray scale change factors between the target edge line and each reference processing edge line, the larger the gray scale change difference value is, the larger the difference of the local gray scale value change condition of the pixel point between each reference processing edge line and the target edge line is, the maximum value of the gray scale change difference values of all the reference processing edge lines is taken as the gray scale change characteristic value of the target edge line, the larger the gray scale change characteristic value is, the larger the difference of the local gray scale value change condition of the pixel point on the target edge line is, and the more possibility that burrs exist on the target edge line is further described.
In one embodiment of the present invention, an absolute value of a difference value of the integral gray scale variation factor between the target edge line and each reference processing edge line may be used as a gray scale variation difference value of each reference processing edge line, so as to implement analysis of the difference of the integral gray scale variation factor between the target edge line and each reference processing edge line.
Step S203: and obtaining the burr possibility of the target edge line according to the integral gray scale change factor and the gray scale change characteristic value of the target edge line, wherein the burr possibility is positively correlated with the gray scale change characteristic value of the target edge line, the burr possibility is negatively correlated with the integral gray scale change factor of the target edge line, and the burr possibility is a normalized numerical value.
The degree of change of the local gray value of the pixel point on the suspected burr edge line formed by the burr is smaller than the suspected burr edge line formed by the noise, and the difference between the degree of change of the local gray value of the pixel point on the processed edge line is larger, so that the obtained overall gray change factor of the target edge line is smaller, and the gray change characteristic value of the target edge line is larger, which means that the more likely the target edge line is formed by the burr, the more likely the burr is present on the target edge line, the less likely the noise is present, and therefore the burr possibility of the target edge line can be obtained according to the overall gray change factor and the gray change characteristic value of the target edge line, wherein the burr possibility is positively correlated with the gray change characteristic value of the target edge line, the burr possibility is negatively correlated with the overall gray change factor of the target edge line, and the burr possibility is a value after normalization treatment.
In one embodiment of the invention, the gray level change characteristic value of the target edge line can be used as a numerator, the whole gray level change factor of the target edge line is used as a denominator, the ratio is used as the burr parameter of the target edge line, and the burr parameter of the target edge line is normalized to obtain the burr possibility of the target edge line.
The expression of the burr likelihood of the target edge line may specifically be, for example:
Wherein, Representing the likelihood of a spur in the target edge line; representing the overall gray scale variation factor of the target edge line; A global gray scale variation factor representing a first reference processed edge line of the target edge line; A second reference processed edge line representing a target edge line; a gray scale variation difference value representing a first reference processed edge line; representing a gray scale variation difference value of a second reference processed edge line; A gradation variation characteristic value representing a target edge line; A spike parameter representing a target edge line; a function representing a maximum value; Representing the normalization function.
Therefore, the possibility of burrs of the target edge line is obtained, the degree of influence of noise on the target edge line can be analyzed based on the possibility of burrs of the target edge line, noise in a gray level image is effectively removed, and the detection precision of burrs on the titanium metal processing surface is improved.
Step S3: acquiring the mass centers of the target edge line and each texture edge line, and taking the distance between the mass centers of the target edge line and each texture edge line as a distance parameter of each texture edge line; selecting a texture edge line with the minimum preset number of distance parameters as a reference texture edge line of the target edge line; and obtaining the burr blurring factor of the target edge line according to the gradient value of each pixel point on each reference texture edge line and the distance parameter of each reference texture edge line.
The method comprises the steps of comparing the burrs with each other, wherein when the burrs are more prominent, the contrast of the area around the burrs is reduced by illumination, the visual effect of the area around the burrs is more fuzzy, the pixel point gradient value on the texture edge line of the area around the burrs is further reduced, therefore, the pixel point gradient value on the texture edge line of the area around the suspected burrs formed by the burrs is relatively smaller, but the pixel point gradient value on the texture edge line of the area around the suspected burrs formed by noise is not greatly changed, therefore, the centroid of the target edge line and each texture edge line can be firstly obtained, the centroid of the edge line in an image is obtained by a technical means well known to a person skilled in the art, the distance between the target edge line and each texture edge line is used as a distance parameter of each texture edge line, the distance between the centroids can be specifically a Euclidean distance, a texture edge line with the smallest preset number of distance parameters is selected as a reference texture edge line positioned around the target edge line, and the fuzzy factor of the target edge line can be calculated and analyzed based on the pixel point gradient value on the reference texture edge line.
The preset number is set to be 20 in one embodiment of the present invention, and the specific value of the preset number may be set by an implementer according to a specific implementation scenario, which is not limited herein.
According to the analysis, the gradient value of the pixel points on the texture edge line of the area around the suspected burr edge line formed by the burrs is relatively small, but the gradient value of the pixel points on the texture edge line of the area around the suspected burr edge line formed by the noises does not change greatly, so that the gradient value of each pixel point on each reference texture edge line can be analyzed, meanwhile, the distance parameter of each reference texture edge line is combined, the burr blurring factor of the target edge line is obtained, the possibility that the target edge line is affected by the burrs instead of the noises is reflected through the burr blurring factor, and the influence degree of the noises on the target edge line is analyzed subsequently based on the burr blurring factor of the target edge line and the obtained burr probability.
Preferably, in one embodiment of the present invention, the method for acquiring the burr possibility of the target edge line specifically includes:
Referring to fig. 4, a flowchart of a method for obtaining a burr blur factor of a target edge line according to an embodiment of the invention is shown.
Step S301: analyzing the overall level of the gradient values of all pixel points on each texture edge line to obtain the gradient distribution value of each texture edge line, and analyzing the overall level of the gradient distribution value of all texture edge lines to obtain the overall gradient distribution parameters.
Firstly, analyzing the overall level of the gradient values of all pixel points on each texture edge line to obtain the gradient distribution value of each texture edge line, reflecting the overall distribution condition of the gradient values of all pixel points on each texture edge line through the gradient distribution value, wherein the gradient values of the pixel points can be calculated through the existing Sobel operator or Scharr operator and other gradient operators, which are not described in detail herein, and further analyzing the overall level of the gradient distribution values of all texture edge lines to obtain overall gradient distribution parameters, reflecting the gradient distribution condition of all texture edge lines through the overall gradient distribution parameters, and providing a data basis for subsequent calculation.
In the embodiment of the invention, the average value or the median of the gradient values of all the pixel points on each texture edge line can be used as the gradient distribution value of each texture edge line to realize the analysis of the overall level of the gradient values of all the pixel points on each texture edge line, which is not limited herein.
In the embodiment of the invention, the average value or the median of the gradient distribution values of all the texture edge lines can be used as the overall gradient distribution parameter to realize the analysis of the overall level of the gradient distribution values of all the texture edge lines, which is not limited herein.
Step S302: obtaining an initial gradient characteristic value of each reference texture edge line according to the overall gradient distribution parameters and the gradient distribution value of each reference texture edge line; the initial gradient characteristic value is positively correlated with the overall gradient distribution parameter, and the initial gradient characteristic value is negatively correlated with the gradient distribution value of each reference texture edge line.
The gradient values of pixel points on the texture edge lines of the area around the suspected burr edge lines formed by the burrs are relatively smaller, so that the gradient distribution value of each reference texture edge line of the target edge line is smaller relative to the overall gradient distribution parameter, the gradient values of pixel points on the texture edge lines of the area around the target edge line are relatively smaller, and further the possibility that the gradient values of the texture edge lines of the area around the target edge line are reduced due to the existence of the burrs is larger, and therefore the initial gradient characteristic value of each reference texture edge line can be obtained according to the overall gradient distribution parameter and the gradient distribution value of each reference texture edge line; the initial gradient characteristic value is positively correlated with the overall gradient distribution parameter, and the initial gradient characteristic value is negatively correlated with the gradient distribution value of each reference texture edge line.
In one embodiment of the present invention, the overall gradient distribution parameter may be used as a numerator, the gradient distribution value of each reference texture edge line may be used as a denominator, and the ratio may be used as the initial gradient feature value of each reference texture edge line.
Step S303: performing negative correlation mapping on the distance parameters of each reference texture edge line to obtain a second distance weight of each reference texture edge line; and weighting the initial gradient characteristic value of each reference texture edge line by using the second distance weight of each reference texture edge line to obtain the real gradient characteristic value of each reference texture edge line.
Considering that the smaller the distance between the centroid of each reference texture edge line and the centroid of the target edge line is, the larger the reference value of the reference texture edge line is, so that the distance parameter of each reference texture edge line can be subjected to negative correlation mapping to obtain a second distance weight of each reference texture edge line, the initial gradient characteristic value of each reference texture edge line is weighted by using the second distance weight of each reference texture edge line to obtain a real gradient characteristic value of each reference texture edge line, and the burr blurring factor of the target edge line can be analyzed based on the real gradient characteristic values of all the reference texture edge lines.
Step S304: analyzing the overall level of the real gradient characteristic values of all the reference texture edge lines, and carrying out normalization processing to obtain the burr blurring factor of the target edge line.
The larger the real gradient characteristic value of the reference texture edge line is, the greater the possibility that the gradient value of the texture edge line of the peripheral area is reduced due to the existence of burrs on the target edge line is, namely the visual effect of the peripheral area is blurred due to the existence of burrs on the target edge line, so that the whole level of the real gradient characteristic values of all the reference texture edge lines of the target edge line can be analyzed and normalized to obtain the burr blurring factor of the target edge line.
In the embodiment of the invention, the analysis of the overall level of the true gradient characteristic values of all the reference texture edge lines of the target edge line can be realized by calculating the average value or the median of the true gradient characteristic values of all the reference texture edge lines of the target edge line, which is not limited herein.
The expression of the burr-blurring factor of the target edge line may specifically be, for example:
Wherein, A spike-blur factor representing a target edge line; representing the first edge line of the object Distance parameters of the edge lines of the reference texture; representing the first edge line of the object A second distance weight for a plurality of reference texture edge lines; representing overall gradient distribution parameters; representing the first edge line of the object Gradient distribution values of the edge lines of the reference texture; representing the first edge line of the object Initial gradient feature values of the reference texture edge lines; representing the first edge line of the object True gradient feature values of the edge lines of the reference texture; Representing the normalization function.
Thus, a burr blurring factor for each target edge line is obtained.
Step S4: obtaining a noise influence factor of the target edge line according to the burr possibility of the target edge line and the difference of the burr blurring factors; and filtering the gray level image based on the noise influence factors of each suspected burr edge line to obtain an enhanced image.
Under the ideal condition that the noise influence is avoided, the burr possibility of the target edge line and the burr blurring factor have positive correlation, the noise breaks the positive correlation, so that the burr possibility of the target edge line and the burr blurring factor have differences, the differences of the burr possibility of the target edge line and the burr blurring factor can be analyzed, the obtained noise influence factors reflect the influence degree of the noise on the target edge line, and the follow-up filtering processing of different intensities on each suspected burr edge line based on the noise influence factors is facilitated, so that the influence of the noise is removed.
Preferably, in one embodiment of the present invention, the method for acquiring the noise influence factor of the target edge line specifically includes:
The larger the influence of noise on the target edge line is, the larger the difference between the burr probability and the burr blurring factor of the target edge line is caused, so that the noise influence factor of the target edge line can be obtained by carrying out normalization processing on the difference between the burr probability and the burr blurring factor of the target edge line.
In one embodiment of the invention, the difference analysis of the target edge line and the burr blur factor can be realized by calculating the absolute value of the difference value of the burr probability and the burr blur factor.
The expression of the noise influence factor of the target edge line may specifically be, for example:
Wherein, A noise impact factor representing a target edge line; Representing the likelihood of a spur in the target edge line; a spike-blur factor representing a target edge line; Representing the normalization function.
The noise influence factor of each suspected burr edge line can be obtained through the same method, and the greater the noise influence factor of each suspected burr edge line is, the greater the influence degree of noise on the suspected burr edge line is, so that the gray level image can be filtered based on the noise influence factor of each suspected burr edge line to obtain an enhanced image, the interference of noise on burr detection is eliminated, and the subsequent detection precision of burrs on the titanium metal processing surface is improved.
Preferably, the method for acquiring the enhanced image in one embodiment of the present invention specifically includes:
According to the embodiment of the invention, a wiener filtering algorithm with better information retention capacity is selected to filter the gray level image, and the greater the noise influence factor is, the greater the influence degree of noise on the suspected burr edge line is, so that the noise influence factor of each suspected burr edge line can be used as a scale factor used by the wiener filtering algorithm, and the region where the minimum circumcircle of each suspected burr edge line in the gray level image is located is filtered based on the wiener filtering algorithm to obtain an enhanced image, so that the target edge lines with different influence degrees of noise are filtered with different intensities, the characteristics of burrs are retained as much as possible while the noise is removed, the subsequent detection precision of burrs on the titanium metal processing surface is improved, and the wiener filtering algorithm is a technical means well known to a person in the art and is not repeated herein.
Therefore, an enhanced image with better quality is obtained, and the existing burr position can be accurately detected in the enhanced image later.
Step S5: and performing edge detection on the enhanced image to obtain a burr part in the enhanced image.
Because the noise in the enhanced image is effectively removed, the enhanced image can be subjected to edge detection, the burr part in the enhanced image is obtained, and the detection precision of the burrs on the titanium metal processing surface is improved.
Preferably, in one embodiment of the present invention, the method for acquiring a burr portion specifically includes:
And (3) carrying out edge detection on the enhanced image based on edge detection algorithms such as a Canny edge detection algorithm and the like to obtain an optimized processing edge line in the enhanced image, wherein the noise in the enhanced image is effectively removed, so that the phenomenon of disconnection and deletion in the optimized processing edge line is caused by burrs, the optimized processing edge line can be compared with a preset processing track, and the part of the optimized processing edge line, which is missing relative to the preset processing track, is used as a burr part in the enhanced image.
One embodiment of the invention provides a burr detection system in a titanium metal machining process, which comprises a memory, a processor and a computer program, wherein the memory is used for storing the corresponding computer program, the processor is used for running the corresponding computer program, and the computer program can realize the method described in the steps S1-S5 when running in the processor.
In summary, the embodiment of the invention firstly obtains the gray level image of the processed titanium metal surface to be detected, and carries out edge detection on the gray level image to obtain the processed edge line, the suspected burr edge line and the texture edge line in the gray level image; obtaining a gray scale change factor of each pixel point according to the distribution of gray scale values of each pixel point in a preset neighborhood taking each pixel point as a center; taking any suspected burr edge line as a target edge line, taking two processing edge lines adjacent to the target edge line as reference processing edge lines of the target edge line, and obtaining the burr possibility of the target edge line according to the gray scale change factors of all pixel points on the target edge line and the difference of the gray scale change factors of the pixel points between the target edge line and the reference processing edge line; acquiring the mass centers of the target edge line and each texture edge line, and taking the distance between the mass centers of the target edge line and each texture edge line as a distance parameter of each texture edge line; selecting a texture edge line with the minimum preset number of distance parameters as a reference texture edge line of the target edge line; obtaining a burr blurring factor of the target edge line according to the gradient value of each pixel point on each reference texture edge line and the distance parameter of each reference texture edge line; obtaining a noise influence factor of the target edge line according to the burr possibility of the target edge line and the difference of the burr blurring factors; filtering the gray level image based on the noise influence factors of each suspected burr edge line to obtain an enhanced image; and performing edge detection on the enhanced image to obtain a burr part in the enhanced image.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (9)
1. A method for detecting burrs in a titanium metal machining process, the method comprising:
acquiring a gray level image of the surface of the titanium metal to be processed, and performing edge detection on the gray level image to obtain a processed edge line, a suspected burr edge line and a texture edge line in the gray level image;
Obtaining a gray scale change factor of each pixel point according to the distribution of gray scale values of each pixel point in a preset neighborhood taking each pixel point as a center; taking any suspected burr edge line as a target edge line, taking two processing edge lines adjacent to the target edge line as reference processing edge lines of the target edge line, and obtaining the burr possibility of the target edge line according to the gray scale change factors of all pixel points on the target edge line and the difference of the gray scale change factors of the pixel points between the target edge line and the reference processing edge line;
Acquiring the mass centers of the target edge line and each texture edge line, and taking the distance between the target edge line and each texture edge line as the distance parameter of each texture edge line; selecting a preset number of texture edge lines with minimum distance parameters as reference texture edge lines of target edge lines; obtaining a burr blurring factor of the target edge line according to the gradient value of each pixel point on each reference texture edge line and the distance parameter of each reference texture edge line;
obtaining a noise influence factor of the target edge line according to the burr possibility of the target edge line and the difference of the burr blurring factors; filtering the gray level image based on the noise influence factors of each suspected burr edge line to obtain an enhanced image;
performing edge detection on the enhanced image to obtain a burr part in the enhanced image;
The obtaining an enhanced image includes:
And taking the noise influence factor of each suspected burr edge line as a scale factor used by a wiener filtering algorithm, and filtering the area where the minimum circumcircle of each suspected burr edge line in the gray level image is located based on the wiener filtering algorithm to obtain an enhanced image.
2. The method for detecting burrs during titanium metal processing as defined in claim 1, wherein said obtaining processing edge lines, suspected burrs edge lines and texture edge lines in gray scale images includes:
Performing edge detection on the gray level image based on an edge detection algorithm to obtain a processing edge line and a texture edge line in the gray level image;
And comparing the processing edge line with a preset processing track, and taking the part of the processing edge line, which is missing relative to the preset processing track, as a suspected burr edge line.
3. The method for detecting burrs during titanium metal processing of claim 1, wherein said obtaining a gray scale variation factor for each pixel point comprises:
Taking any pixel point in the gray level image as a target pixel point, and taking any pixel point in a preset adjacent area taking the target pixel point as a center as a pixel point to be measured; if the gray value of the pixel to be detected is greater than or equal to the gray value of all the pixels in a preset window taking the pixel to be detected as the center, marking the pixel to be detected as a maximum pixel, and acquiring all the maximum pixels in a preset adjacent area;
taking any one maximum pixel point as a target maximum pixel point, and taking other maximum pixel points closest to the target maximum pixel point as reference maximum pixel points of the target maximum pixel point;
taking the average value of the gray values of the target maximum pixel point and the corresponding reference maximum pixel point as a first gray parameter of the target maximum pixel point; analyzing the integral level of gray values of all pixel points on the connecting line between the target maximum pixel point and the corresponding reference maximum pixel point to obtain a second gray parameter of the target maximum pixel point; acquiring an initial gray scale variation parameter of a target maximum pixel point according to the first gray scale parameter and the second gray scale parameter, wherein the initial gray scale variation parameter is positively correlated with the first gray scale parameter, and the initial gray scale variation parameter is negatively correlated with the second gray scale parameter;
Performing negative correlation mapping on the distance between the target maximum pixel point and the corresponding reference maximum pixel point to obtain a first distance weight of the target maximum pixel point, and weighting the initial gray level change parameter by using the first distance weight of the target maximum pixel point to obtain a real gray level change parameter of the target maximum pixel point;
analyzing the overall level of the real gray scale change parameters of all the maximum pixel points in the preset neighborhood, and carrying out normalization processing to obtain the gray scale change factor of the target pixel point.
4. The method for detecting burrs during titanium metal processing of claim 1, wherein said obtaining the probability of burrs of a target edge line comprises:
analyzing the overall level of the gray scale change factors of all pixel points on the target edge line or each reference processing edge line to obtain the overall gray scale change factors of the target edge line or each reference processing edge line;
According to the difference of the integral gray scale change factors between the target edge line and each reference processing edge line, gray scale change difference values of each reference processing edge line are obtained, and the maximum value of the gray scale change difference values of all the reference processing edge lines is used as a gray scale change characteristic value of the target edge line;
and obtaining the burr possibility of the target edge line according to the integral gray scale change factor and the gray scale change characteristic value of the target edge line, wherein the burr possibility is positively correlated with the gray scale change characteristic value of the target edge line, the burr possibility is negatively correlated with the integral gray scale change factor of the target edge line, and the burr possibility is a numerical value after normalization processing.
5. The method for detecting burrs during titanium metal processing of claim 1, wherein said obtaining a burr blur factor of a target edge line comprises:
Analyzing the overall level of the gradient values of all pixel points on each texture edge line to obtain a gradient distribution value of each texture edge line, and analyzing the overall level of the gradient distribution value of all texture edge lines to obtain an overall gradient distribution parameter;
Obtaining an initial gradient characteristic value of each reference texture edge line according to the overall gradient distribution parameters and the gradient distribution value of each reference texture edge line; the initial gradient characteristic value is positively correlated with the overall gradient distribution parameter, and the initial gradient characteristic value is negatively correlated with the gradient distribution value of each reference texture edge line;
performing negative correlation mapping on the distance parameters of each reference texture edge line to obtain a second distance weight of each reference texture edge line; weighting the initial gradient characteristic value of each reference texture edge line by using the second distance weight of each reference texture edge line to obtain a real gradient characteristic value of each reference texture edge line;
analyzing the overall level of the real gradient characteristic values of all the reference texture edge lines, and carrying out normalization processing to obtain the burr fuzzy factor of the target edge line.
6. The method for detecting burrs during titanium metal processing of claim 1, wherein said obtaining noise influencing factors of a target edge line comprises:
And carrying out normalization processing on the difference of the burr possibility and the burr blurring factor according to the target edge line to obtain a noise influence factor of the target edge line.
7. The method for detecting burrs in a titanium metal working process of claim 1, wherein said performing edge detection on the enhanced image to obtain burrs in the enhanced image comprises:
performing edge detection on the enhanced image to obtain an optimized processing edge line in the enhanced image;
Comparing the optimized machining edge line with a preset machining track, and taking the missing part of the optimized machining edge line relative to the preset machining track as a burr part in the enhanced image.
8. The method for detecting burrs in a titanium metal machining process according to claim 1, wherein the preset number is an integer ranging from 10 to 30.
9. A system for detecting burrs during titanium metal machining, said system comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor implements the steps of the method according to any one of claims 1-8 when executing said computer program.
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