CN116503408A - Scanning technology-based steel structure surface defect detection method - Google Patents
Scanning technology-based steel structure surface defect detection method Download PDFInfo
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Abstract
The application belongs to the technical field of image processing, and provides a steel structure surface defect detection method based on a scanning technology, which comprises the following steps: acquiring a surface image of the steel structure through a scanning technology; denoising the surface image to obtain a surface gray level image; dividing the surface gray level image by a Canny operator to obtain a weld joint framework; analyzing the weld joint skeleton to obtain a weld joint defect significance degree index; inputting the significance degree index into a visual significance detection Itti algorithm to obtain a fusion defect significance map. By the method provided by the invention, the situation that the image defects cannot be identified due to the fact that the weld defects are not obvious is avoided, and the detection precision of the surface defects of the steel structure is improved.
Description
Technical Field
The application relates to the technical field of image processing, in particular to a steel structure surface defect detection method based on a scanning technology.
Background
The steel structure is widely applied to industries such as manufacturing, building, aviation and the like, and is an important component of national economy.
In the production and use process of the steel structure, the steel structure is affected by factors such as processing equipment, production environment and the like, and defects are inevitably generated. In the use process of the steel structure, the steel materials are connected with each other generally through welding so as to realize firm connection between the steel materials, and defect characteristics such as weld fracture, uneven weld width and uneven height and the like can be generated by technical workers due to personal technical problems when welding the weld, and the defect characteristics of the weld are generally judged through a traditional image processing method, but the weld with less obvious defect characteristics is difficult to identify through the traditional image processing method.
Thus, there is a need for a method to identify welds with less pronounced surface defect characteristics.
Disclosure of Invention
The application provides a steel structure surface defect detection method based on a scanning technology, so as to improve the steel structure surface defect detection precision.
There is provided a method for detecting surface defects of a steel structure based on a scanning technique, the method comprising:
acquiring a surface image of the steel structure through a scanning technology;
denoising the surface image to obtain a surface gray level image;
dividing the surface gray level image through a Canny operator to obtain a weld joint framework;
analyzing the weld joint skeleton to obtain a weld joint defect significance degree index;
and inputting the significance degree index into a visual significance detection Itti algorithm to obtain a fusion defect significance map.
In some embodiments of the present invention, segmenting the surface grayscale image by a Canny operator to obtain a weld skeleton comprises:
dividing the surface gray level image through a Canny operator to obtain a welding line region;
refining the welding line area by a non-maximum value inhibition method to obtain a welding line framework;
and redundant lines in the weld joint framework are filtered by adopting double threshold values, so that the continuity of the weld joint framework is ensured.
In some embodiments of the present invention, analyzing the weld skeleton to obtain a weld defect significance level indicator includes:
gray value of pixel point of weld joint skeletonIn the region of the weld skeleton in the longitudinal direction thereofThe maximum value in the gray values of the pixel points is differenced to obtain the gray offset of the weld joint framework；
According to the gray level offset of the weld joint frameworkAnd weld seam skeleton seam widthCalculating to obtain the width offset of the welding seam；
Obtaining the shadow gray level of the weld joint framework according to the gray values of the pixel points of the weld joint areas on the upper side and the lower side of the weld joint framework；
According to the width deviation of the welding lineAnd the degree of shade gray of the weld skeletonObtaining the width and height offset of the weld joint framework；
A gray scale run matrix method is adopted, and gray scale values of each pixel point in the neighborhood of the pixel points of the weld joint skeleton are calculated to obtain a gray scale run matrix;
according to the gray scale run matrix, calculating and obtaining the fracture degree of the welding joint；
Height deviation according to seam width of weld joint frameworkAnd degree of weld joint fractureObtaining the degree of abnormality at the pixel points of the weld skeleton；
According to the degree of abnormality at the pixel points of the weld joint frameworkObtaining the defect significance degree index of the welding line。
In some embodiments of the invention, the weld skeleton gray scale offsetThe calculation method of (1) is as follows:
in the method, in the process of the invention,representing the first weld joint skeletonThe gray scale offset of the weld joint skeleton at each pixel point,representing the longitudinal direction of the pixel points of the weld joint skeletonThe gray value of the largest pixel among the gray values,representing the first weld joint skeletonGray values at the individual pixel points.
In some embodiments of the invention, the weld width offsetThe calculation method of (1) is as follows:
in the method, in the process of the invention,representing the first weld joint skeletonThe weld width offset at each pixel point,representing the first weld joint skeletonThe gray scale offset of the weld joint skeleton at each pixel point,representing the first weld joint skeletonAbsolute value of the difference between the slit width at each pixel point and the average slit width.
In some embodiments of the invention, the weld skeleton shading gray levelThe calculation method of (1) is as follows:
in the method, in the process of the invention,representing the first weld joint skeletonThe degree of shade gray of the weld skeleton at each pixel point,representing the first weld joint skeletonThe sum of gray values of pixels in the weld area upwards from each pixel,representing the first weld joint skeletonAnd the gray value sum of the pixels in the weld joint area with the downward pixels.
In some embodiments of the invention, the weld backbone seam width height offsetThe calculation method comprises the following steps:
in the method, in the process of the invention,representing the first weld joint skeletonThe seam width and height offset of the weld joint skeleton at each pixel point,representing the first weld joint skeletonThe weld width offset at each pixel point,representing the first weld joint skeletonThe degree of shade gray of the weld skeleton at each pixel point,and the average value of the shade gray level of the weld joint skeleton of the pixel points on the weld joint skeleton is represented.
In some embodiments of the invention, the weld joint fracture degreeThe calculation method of (1) is as follows:
in the method, in the process of the invention,representing the first weld joint skeletonThe degree of weld joint fracture at each pixel point,is thatIs a matrix of gray scale run lengths of (c),representing the gray level in the 5x5 neighborhood,representing the direction alongIs used for the maximum run length of the (c) code,representing the direction alongThe sum of all elements in the gray run length matrix above.
In some embodiments of the invention, the degree of abnormality at the weld skeleton pixel pointsThe calculation method of (1) is as follows:
in the method, in the process of the invention,representing the first weld joint skeletonThe degree of abnormality at each pixel point,representing the first weld joint skeletonThe seam width and height offset of the weld joint skeleton at each pixel point,representing the first weld joint skeletonThe degree of weld joint fracture at each pixel point.
In some embodiments of the invention, the weld defect significance level indicatorThe calculation method of (1) is as follows:
in the method, in the process of the invention,the index of the significance degree of the weld defect is shown,representing the first weld joint skeletonDegree of abnormality at each pixel point.
As can be seen from the above embodiments, the scanning technology-based steel structure surface defect detection method provided by the embodiments of the present application has the following beneficial effects:
according to the invention, the significance detection is carried out according to the characteristic of the width and height deviation of the welding seam and the fracture degree of the welding seam joint of the pixel points on the welding seam framework, the image defect characteristic which is not easy to be identified by a machine is analyzed, and on the basis that the brightness, the color and the direction of the Itti algorithm are fused together to form a defect significance map, the index of the significance degree of the welding seam defect is added as an index, and the index of the significance degree of the welding seam defect is fused together to form the defect significance map, so that the welding seam defect on the surface of the steel structure image is more significantly represented. The condition that image defects cannot be identified due to unobvious weld defects is avoided, and the detection precision of the surface defects of the steel structure is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a basic flow diagram of a method for detecting a surface defect of a steel structure based on a scanning technique according to an embodiment of the present application;
FIG. 2 is a schematic flow diagram of a method for obtaining a weld skeleton according to an embodiment of the present disclosure;
fig. 3 is a basic flow chart of a method for obtaining a defect significance level index of a weld according to an embodiment of the present application.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The following describes in detail a method for detecting a surface defect of a steel structure based on a scanning technique according to this embodiment with reference to the accompanying drawings.
Fig. 1 is a basic flow chart of a method for detecting a surface defect of a steel structure based on a scanning technique according to an embodiment of the present application, as shown in fig. 1, the method specifically includes the following steps:
s100: and acquiring and obtaining a surface image of the steel structure through a scanning technology.
And (3) carrying out high-speed scanning imaging on the surface of the steel structure by using a linear sensor and an image processor which move at high speed through a surface scanning camera, and acquiring and obtaining a surface image of the steel structure, namely a surface RGB space image of the steel structure.
S200: and denoising the surface image to obtain a surface gray level image.
And denoising pretreatment is carried out on the obtained surface image of the steel structure, so that the influence caused by noise and partial external interference is eliminated, and the accuracy of subsequent analysis is enhanced. In order to remove noise while retaining boundary information, the invention selects median filtering to perform denoising processing on the image, and an implementer can also adopt other denoising methods. And converting the acquired RGB image of the steel structure surface into a steel structure surface gray scale image.
S300: and dividing the surface gray level image through a Canny operator to obtain a weld joint framework.
The welding level and the technique of each worker are different, the height and the width deviation of the welding seam welded by one worker are also different, and the difference is difficult to judge whether the welding seam quality is qualified by a manual detection method, so that the welding seam quality needs to be analyzed by adopting an image processing technology.
The defects in the surface image of the steel structure are various, and particularly, poor weld joint formation caused by different welding methods of welding workers is mainly represented by three bad states of poor joints, inconsistent seam widths and inconsistent seam heights. If these defect states are detected only manually, the evaluation results are easily affected by subjective factors. In view of this, the present invention builds a severity index for three features at the weld by three bad conditions of weld formation to evaluate if the weld should be more significant.
Fig. 2 is a basic flow chart of a method for obtaining a weld bead skeleton according to an embodiment of the present application, as shown in fig. 2, in some embodiments of the present invention, a weld bead skeleton is obtained by segmenting a surface gray image by a Canny operator, including:
s301: dividing the surface gray level image by a Canny operator to obtain a welding seam region;
s302: refining the welding line area by a non-maximum value inhibition method to obtain a welding line framework;
s303: and redundant lines in the weld joint framework are filtered by adopting double threshold values, so that the continuity of the weld joint framework is ensured.
S400: and analyzing the weld joint framework to obtain the weld joint defect significance degree index.
The weld defect significance degree index of each pixel point is obtained by analyzing three characteristics of the weld width deviation degree of each pixel point on the weld skeleton, the weld width height deviation degree of the weld skeleton and the fracture degree of the weld joint。
Fig. 3 is a basic flow chart of a method for obtaining a defect significance level index of a weld according to an embodiment of the present application, as shown in fig. 3, in some embodiments of the present invention, a weld skeleton is analyzed to obtain a defect significance level index of the weld, including the following steps:
s401: weld joint boneGray value of pixel point of frameIn the region of the weld skeleton in the longitudinal direction thereofThe maximum value in the gray values of the pixel points is differenced to obtain the gray offset of the weld joint framework。
Assume that the number of pixel points on the weld skeleton isThe number of the two-dimensional space-saving type,representing the first weld joint skeletonGray values of individual pixels. By welding the first joint on the frameworkTaking a pixel point as an example, assume that the first pixel point is on a welding line frameworkThe longitudinal axis direction of each pixel point is provided withA pixel point, for the first on the weld joint frameworkGray value of each pixel pointAccording to the pixel point and the welding line framework area in the longitudinal directionThe maximum value in the gray values of the pixel points is differed, the width direction of the weld joint framework is defined as the longitudinal direction, and the first weld joint framework is obtainedWeld joint skeleton gray scale offset at each pixel point. Further, the gray level offset of the weld joint frameworkThe calculation method of (1) is as follows:
in the method, in the process of the invention,representing the first weld joint skeletonThe gray scale offset of the weld joint skeleton at each pixel point,representing the longitudinal direction of the pixel points of the weld joint skeletonThe gray value of the largest pixel among the gray values,representing the first weld joint skeletonGray values at the individual pixel points.
Gray scale offset of weld joint frameworkReflecting whether each pixel point on the weld skeleton is the center of the weld width, ifThe larger the pixel points of the weld joint skeleton are, the larger the center of the offset weld joint width is, which indicates that the weld joint width is uneven and the quality of the weld joint width is poor; on the contrary, sayThe width of the welding line is uniform, and the quality of the welding line is good.
S402: according to the gray level offset of the weld joint frameworkAnd weld seam skeleton seam widthCalculating to obtain the width offset of the welding seam。
By welding the first joint on the frameworkFor example, according to the first pixel on the weld skeletonWeld joint skeleton gray scale offset at each pixel pointAnd the first weld joint frameworkWeld bead skeleton seam width at individual pixel pointsCalculating to obtain the first weld joint skeletonWeld width offset at individual pixel points. Wherein, the first welding line framework isThe number of the pixel points in the welding seam framework area along the longitudinal axis direction of the position of each pixel point is the width of the welding seam frameworkLikewise, the weld bead skeleton width direction is defined hereinIs in the direction of the longitudinal axis. Further, the first welding line frameworkWeld width offset at individual pixel pointsThe calculation method of (1) is as follows:
in the method, in the process of the invention,representing the first weld joint skeletonThe weld width offset at each pixel point,representing the first weld joint skeletonThe gray scale offset of the weld joint skeleton at each pixel point,representing the first weld joint skeletonAbsolute value of the difference between the weld skeleton seam width at each pixel point and the average weld skeleton seam width.
If it isThe larger the welding seam framework pixel point is, the farther the welding seam framework pixel point is from the highest salient point, and the larger the average width of the welding seam at the position of the welding seam framework pixel point is, the worse the quality of the welding seam width is; otherwise, the weld width quality is better.
S403: obtaining the shadow gray level of the weld joint framework according to the gray values of the pixel points of the weld joint areas on the upper side and the lower side of the weld joint framework。
And the joint height characteristic of each pixel point on the weld joint framework can be analyzed by obtaining the sum of the gray values of the pixel points on the smallest side of the weld joint framework. By welding the first joint on the frameworkFor example, according to the first pixel on the weld skeletonGray values of pixel points of welding line areas on the upper side and the lower side of each pixel point are obtained, and the first welding line skeleton is obtainedShadow gray scale of weld joint skeleton at each pixel point. Further, the shade gray level of the weld joint frameworkThe calculation method of (1) is as follows:
in the method, in the process of the invention,representing the first weld joint skeletonThe degree of shade gray of the weld skeleton at each pixel point,representing the first weld joint skeletonThe sum of gray values of pixels in the weld area upwards from each pixel,representing the first weld joint skeletonAnd the gray value sum of the pixels in the weld joint area with the downward pixels.
If it isThe bigger the height of the pixel point of the weld joint skeleton is, the brighter the shadow part is, and the gray value is larger; otherwise, the height of the weld joint skeleton pixel point is higher.
S404: according to the width deviation of the welding lineAnd the degree of shade gray of the weld skeletonObtaining the width and height offset of the weld joint framework。
The calculation of the height of a single weld bead skeleton pixel does not allow for the assessment of weld bead height quality by calculating the degree of weld bead skeleton shadow gray scale for each weld bead skeleton pixelMean value of gray scale degree of shadow of weld joint frameworkThe square of the difference and the width deviation of the welding line are addedObtaining the width and height offset of each welding seam skeleton pixel spot welding seam. By welding the first joint on the frameworkThe first pixel point is taken as an example on a welding line frameworkSeam width and height offset of weld joint skeleton at each pixel pointThe calculation method comprises the following steps:
in the method, in the process of the invention,representing the first weld joint skeletonThe seam width and height offset of the weld joint skeleton at each pixel point,representing the first weld joint skeletonThe weld width offset at each pixel point,representing the first weld joint skeletonThe degree of shade gray of the weld skeleton at each pixel point,and the average value of the shade gray level of the weld joint skeleton of the pixel points on the weld joint skeleton is represented.
If it isThe larger the welding seam framework pixel point is, the larger the deviation of the welding seam framework pixel point from the highest salient point is, and meanwhile, the larger the difference of the welding seam width of the welding seam framework pixel point is compared with the average width, the worse the quality of the welding seam width is; otherwise, the slit width quality is better.The evaluation of the pixel point height of each weld joint skeleton is shown, if the value is larger, the pixel point height of the weld joint skeleton is higher or lower than the average weld joint height, and the quality of the weld joint is poorer; otherwise, the high quality of the seam at the pixel point of the welding seam framework is indicated.Representing the width and height offset of the pixel spot weld of the weld skeleton ifThe bigger the seam is, the wider the seam width of the weld joint skeleton is, the higher the seam quality is poor; otherwise, the seam width and the seam high quality of the weld joint framework are better.
S405: and calculating the gray value of each pixel point in the neighborhood of the pixel point of the weld joint skeleton by adopting a gray run matrix method to obtain a gray run matrix.
In order to obtain the gray value non-uniformity around each weld joint skeleton pixel, the situation that the joint is poor and not fused around part of the weld joint skeleton pixels is possible. The gray value of each pixel point of the welding line area is mapped to the gray level of 0-4 in equal proportion and average to obtain a gray image with the gray level of 5。
For each weld skeleton pixel point in the weld region after gray level conversionAdopting a gray scale run matrix method to weld the pixel points of the skeletonAnd calculating the gray value of each pixel point in the neighborhood to obtain a gray run matrix.
S406: according to the gray scale run matrix, calculating and obtaining the fracture degree of the welding joint。
By welding the first joint on the frameworkTaking a pixel point as an example, calculating to obtain the first pixel on the weld joint framework according to the gray scale run matrixDegree of weld joint fracture at individual pixel points. Further, the first welding line frameworkDegree of weld joint fracture at individual pixel pointsThe calculation method of (1) is as follows:
in the method, in the process of the invention,representing the first weld joint skeletonThe degree of weld joint fracture at each pixel point,is thatIs a matrix of gray scale run lengths of (c),representing the gray level in the 5x5 neighborhood,representing the direction alongIs used for the maximum run length of the (c) code,representing the direction alongThe sum of all elements in the gray run length matrix above.
Q represents the fracture degree of the weld joint, namely the uneven distribution of pixel gray levels around each weld skeleton pixel point. If it isThe larger the pixel points are, the uneven gray distribution of the pixel points around the pixel points of the current weld joint skeleton is shown, and the state that the joint defect exists in the larger probability around the pixel points of the weld joint skeleton is shown; otherwise, the state that the joint is poor in a small probability exists around the pixel point of the weld joint skeleton is indicated.
S407: height deviation according to seam width of weld joint frameworkAnd degree of weld joint fractureObtaining the degree of abnormality at the pixel points of the weld skeleton。
By welding the first joint on the frameworkFor example, according to the first pixel on the weld skeletonSeam width and height offset of weld joint skeleton at each pixel pointAnd degree of weld joint fractureObtaining a weld joint frameworkDegree of abnormality at each pixel point. Further, the first welding line frameworkDegree of abnormality at each pixel pointThe calculation method of (1) is as follows:
in the method, in the process of the invention,representing the first weld joint skeletonThe degree of abnormality at each pixel point,representing the first weld joint skeletonThe seam width and height offset of the weld joint skeleton at each pixel point,representing the first weld joint skeletonThe degree of weld joint fracture at each pixel point.
In the range of 0-1. If it isThe larger the weld joint skeleton pixel point, the higher the seam, the wider the seam, and the quality abnormality of joints, etc., indicating the possible existence of the weld joint skeleton pixel pointDefects; and otherwise, the probability of defects at the pixel points of the weld joint framework is smaller.
S408: according to the degree of abnormality at the pixel points of the weld joint frameworkObtaining the defect significance degree index of the welding line。
By welding the first joint on the frameworkFor example, according to the first pixel on the weld skeletonDegree of abnormality at each pixel pointObtaining the defect significance degree index of the welding line. Further, the defect significance degree index of the welding lineThe calculation method of (1) is as follows:
in the method, in the process of the invention,the index of the significance degree of the weld defect is shown,representing the first weld joint skeletonDegree of abnormality at each pixel point.
Weld defect significance level indexBy summing the degree of abnormality of the weld skeleton pixels, ifThe closer to larger, the greater the likelihood that the weld region is defective, requiring the region to be more significantly represented; and conversely, the less likely the weld region is to be defective. The judgment threshold may be empirically set.
S500: inputting the significance degree index into a visual significance detection Itti algorithm to obtain a fusion defect significance map.
Using a visual saliency detection Itti algorithm, wherein the Itti algorithm is a saliency target detection algorithm, and inputting a saliency degree index into the visual saliency detection Itti algorithm, namely inputting a weld defect saliency degree indexAdded to the brightnessColor ofAnd direction ofIn the comprehensive significance map evaluation of (2), obtainIs a final fusion defect saliency map. The weld defects were evaluated in this way.
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.
It should be noted that unless otherwise specified and limited, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such article or apparatus. Without further limitation, the statement "comprises one … …" does not exclude that an additional identical element is present in an article or device that comprises the element. In addition, the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. The method for detecting the surface defects of the steel structure based on the scanning technology is characterized by comprising the following steps of:
acquiring a surface image of the steel structure through a scanning technology;
denoising the surface image to obtain a surface gray level image;
dividing the surface gray level image through a Canny operator to obtain a weld joint framework;
analyzing the weld joint skeleton to obtain a weld joint defect significance degree index;
and inputting the significance degree index into a visual significance detection Itti algorithm to obtain a fusion defect significance map.
2. The scanning technology-based steel structure surface defect detection method according to claim 1, wherein the surface gray scale image is segmented by a Canny operator to obtain a weld skeleton, comprising:
dividing the surface gray level image through a Canny operator to obtain a welding line region;
refining the welding line area by a non-maximum value inhibition method to obtain a welding line framework;
and redundant lines in the weld joint framework are filtered by adopting double threshold values, so that the continuity of the weld joint framework is ensured.
3. The method for detecting the surface defects of the steel structure based on the scanning technology according to claim 1, wherein the analysis of the weld skeleton to obtain the weld defect significance level index comprises the following steps:
gray value of pixel point of weld joint skeletonIn the region of the weld-seam skeleton in the longitudinal direction thereof>The maximum value in the gray values of the pixel points is differenced to obtain the gray offset degree of the weld joint skeleton>；
According to the gray level offset of the weld joint frameworkAnd weld skeleton seam width->Calculating to obtain the width deviation of the welding line>；
Obtaining the shadow of the weld joint framework according to the gray values of the pixel points of the weld joint areas on the upper side and the lower side of the weld joint frameworkGray scale level；
According to the width deviation of the welding lineAnd the shade gray level of the weld joint skeleton->Obtaining the seam width and height deviation of the weld joint framework>；
A gray scale run matrix method is adopted, and gray scale values of each pixel point in the neighborhood of the pixel points of the weld joint skeleton are calculated to obtain a gray scale run matrix;
according to the gray scale run matrix, calculating and obtaining the fracture degree of the welding joint；
Height deviation according to seam width of weld joint frameworkAnd degree of weld joint fracture->Obtaining the degree of abnormality +.>；
According to the degree of abnormality at the pixel points of the weld joint frameworkObtaining a weld defect significance degree index ++>。
4. The method for detecting surface defects of steel structure based on scanning technology as claimed in claim 3, wherein the gray level offset of the weld skeletonThe calculation method of (1) is as follows:
in the method, in the process of the invention,indicating the%>The gray scale offset of the weld joint skeleton at each pixel point,pixel point longitudinal direction of weld joint skeleton>Gray value of the largest pixel of the gray values,/gray value of the pixel of the gray values>Indicating the%>Gray values at the individual pixel points.
5. The method for detecting surface defects of steel structure based on scanning technology according to claim 3, wherein the weld width deviationThe calculation method of (1) is as follows:
in the method, in the process of the invention,indicating the%>Weld width offset at individual pixels, +.>Indicating the%>Gray scale offset of weld skeleton at each pixel point, < ->Indicating the%>Absolute value of the difference between the slit width at each pixel point and the average slit width.
6. The method for detecting surface defects of steel structure based on scanning technology according to claim 3, wherein the gray level of shadow of the weld skeletonThe calculation method of (1) is as follows:
in the method, in the process of the invention,indicating the%>The gray level of the weld skeleton shadow at each pixel point,/->Indicating the%>Gray value sum of weld joint region pixel points upwards of each pixel point, < ->Indicating the%>And the gray value sum of the pixels in the weld joint area with the downward pixels.
7. The method for detecting surface defects of steel structure based on scanning technology as claimed in claim 3, wherein the width and height deviation of the weld bead skeleton is as followsThe calculation method comprises the following steps:
in the method, in the process of the invention,indicating the%>Seam width and height offset of weld joint skeleton at each pixel point, < >>Indicating the%>Personal imageWeld width offset at plain +.>Indicating the%>The gray level of the weld skeleton shadow at each pixel point,/->And the average value of the shade gray level of the weld joint skeleton of the pixel points on the weld joint skeleton is represented.
8. The method for detecting surface defects of steel structures based on the scanning technology as recited in claim 3, wherein the degree of fracture of the weld jointThe calculation method of (1) is as follows:
in the method, in the process of the invention,indicating the%>Degree of weld joint fracture at individual pixel points, +.>Is thatGray run length matrix of>Represents gray levels in the 5x5 neighborhood, < >>Indicating>Is the maximum run length of +.>Indicating>The sum of all elements in the gray run length matrix above.
9. The method for detecting surface defects of steel structures based on the scanning technique as recited in claim 3, wherein the degree of abnormality at the pixel points of the weld skeletonThe calculation method of (1) is as follows:
in the method, in the process of the invention,indicating the%>Degree of abnormality at each pixel, +.>Indicating the%>Seam width and height offset of weld joint skeleton at each pixel point, < >>Representing the first weld joint skeleton/>The degree of weld joint fracture at each pixel point.
10. The method for detecting surface defects of steel structures based on the scanning technology as recited in claim 3, wherein the weld defect significance level indexThe calculation method of (1) is as follows:
in the method, in the process of the invention,index of significance of the weld defect, +.>Indicating the%>Degree of abnormality at each pixel point.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001011545A1 (en) * | 1999-08-09 | 2001-02-15 | Cross Match Technologies, Inc. | Calibration and correction in a fingerprint scanner |
| CN102413283A (en) * | 2011-10-25 | 2012-04-11 | 广州飒特红外股份有限公司 | Infrared chart digital signal processing system and method |
| JP2012202858A (en) * | 2011-03-25 | 2012-10-22 | Railway Technical Research Institute | Closure crack detection method of concrete surface |
| WO2014139273A1 (en) * | 2013-03-14 | 2014-09-18 | 苏州华源包装股份有限公司 | Weld seam defect detection method |
| CN105488758A (en) * | 2015-11-30 | 2016-04-13 | 河北工业大学 | Image scaling method based on content awareness |
| US20160231254A1 (en) * | 2013-10-11 | 2016-08-11 | Bizerba Luceo | Method and device for inspecting packaging welds |
| CN105931227A (en) * | 2016-04-14 | 2016-09-07 | 北京工业大学 | Weld surface defect feature extraction method based on image gray scale B sweeping curve |
| US20170221201A1 (en) * | 2016-01-28 | 2017-08-03 | Taihao Medical Inc. | Medical image processing apparatus and breast image processing method thereof |
| CN112907595A (en) * | 2021-05-06 | 2021-06-04 | 武汉科技大学 | Surface defect detection method and device |
| US20210204898A1 (en) * | 2020-01-03 | 2021-07-08 | National Taiwan University | Medical image analyzing system and method thereof |
| WO2021168733A1 (en) * | 2020-02-27 | 2021-09-02 | 京东方科技集团股份有限公司 | Defect detection method and apparatus for defect image, and computer-readable storage medium |
| CN115170570A (en) * | 2022-09-07 | 2022-10-11 | 南通睿谷纺织科技有限公司 | Fabric fuzzing and pilling detection method based on gray level run-length matrix |
| WO2022225995A1 (en) * | 2021-04-19 | 2022-10-27 | F. Hoffmann-La Roche Ag | Methods and systems for gene alteration prediction from pathology slide images |
| CN115375676A (en) * | 2022-10-24 | 2022-11-22 | 微山三利特不锈钢有限公司 | Stainless steel product quality detection method based on image recognition |
| US20230092247A1 (en) * | 2021-09-22 | 2023-03-23 | Rockwell Automation Technologies, Inc. | Automated monitoring using image analysis |
-
2023
- 2023-06-28 CN CN202310770671.5A patent/CN116503408B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001011545A1 (en) * | 1999-08-09 | 2001-02-15 | Cross Match Technologies, Inc. | Calibration and correction in a fingerprint scanner |
| JP2012202858A (en) * | 2011-03-25 | 2012-10-22 | Railway Technical Research Institute | Closure crack detection method of concrete surface |
| CN102413283A (en) * | 2011-10-25 | 2012-04-11 | 广州飒特红外股份有限公司 | Infrared chart digital signal processing system and method |
| WO2014139273A1 (en) * | 2013-03-14 | 2014-09-18 | 苏州华源包装股份有限公司 | Weld seam defect detection method |
| US20160231254A1 (en) * | 2013-10-11 | 2016-08-11 | Bizerba Luceo | Method and device for inspecting packaging welds |
| CN105488758A (en) * | 2015-11-30 | 2016-04-13 | 河北工业大学 | Image scaling method based on content awareness |
| US20170221201A1 (en) * | 2016-01-28 | 2017-08-03 | Taihao Medical Inc. | Medical image processing apparatus and breast image processing method thereof |
| CN105931227A (en) * | 2016-04-14 | 2016-09-07 | 北京工业大学 | Weld surface defect feature extraction method based on image gray scale B sweeping curve |
| US20210204898A1 (en) * | 2020-01-03 | 2021-07-08 | National Taiwan University | Medical image analyzing system and method thereof |
| WO2021168733A1 (en) * | 2020-02-27 | 2021-09-02 | 京东方科技集团股份有限公司 | Defect detection method and apparatus for defect image, and computer-readable storage medium |
| WO2022225995A1 (en) * | 2021-04-19 | 2022-10-27 | F. Hoffmann-La Roche Ag | Methods and systems for gene alteration prediction from pathology slide images |
| CN112907595A (en) * | 2021-05-06 | 2021-06-04 | 武汉科技大学 | Surface defect detection method and device |
| US20230092247A1 (en) * | 2021-09-22 | 2023-03-23 | Rockwell Automation Technologies, Inc. | Automated monitoring using image analysis |
| CN115170570A (en) * | 2022-09-07 | 2022-10-11 | 南通睿谷纺织科技有限公司 | Fabric fuzzing and pilling detection method based on gray level run-length matrix |
| CN115375676A (en) * | 2022-10-24 | 2022-11-22 | 微山三利特不锈钢有限公司 | Stainless steel product quality detection method based on image recognition |
Non-Patent Citations (5)
| Title |
|---|
| CHUANG SHAO: "A Kind of Pavement Crack Detection Method Based on Digital Image Processing", 《 2019 IEEE 4TH ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC)》 * |
| ZONGYUAN XIE: "Surface crack width measurement based on digital image processing", 《2022 34TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC)》 * |
| 刘丽;郭新兴;姚海芳;周杰;: "基于图像信息融合的焊缝无损探伤研究", 电子设计工程, no. 18 * |
| 张晓光, 徐健健, 任世锦: "基于字典的概率松弛法对焊缝图像中裂纹缺陷的提取", 华东理工大学学报, no. 05 * |
| 罗立浩;郑日荣;何小敏;许亮;: "基于机器视觉的电机换向器质量在线检测系统开发", 计算机测量与控制, no. 07 * |
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Denomination of invention: Surface defect detection method for steel structures based on scanning technology Effective date of registration: 20231225 Granted publication date: 20230825 Pledgee: Qufu sub branch of Postal Savings Bank of China Ltd. Pledgor: Qufu Yuanda Group Engineering Co.,Ltd. Registration number: Y2023980073867 |