CN114486732A - Ceramic tile defect online detection method based on line scanning three-dimension - Google Patents
Ceramic tile defect online detection method based on line scanning three-dimension Download PDFInfo
- Publication number
- CN114486732A CN114486732A CN202111651839.8A CN202111651839A CN114486732A CN 114486732 A CN114486732 A CN 114486732A CN 202111651839 A CN202111651839 A CN 202111651839A CN 114486732 A CN114486732 A CN 114486732A
- Authority
- CN
- China
- Prior art keywords
- tile
- data
- defect
- feature
- dimensional
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000007547 defect Effects 0.000 title claims abstract description 168
- 239000000919 ceramic Substances 0.000 title claims abstract description 112
- 238000001514 detection method Methods 0.000 title claims abstract description 71
- 238000000605 extraction Methods 0.000 claims abstract description 39
- 238000006243 chemical reaction Methods 0.000 claims abstract description 14
- 238000000034 method Methods 0.000 claims description 26
- 238000005259 measurement Methods 0.000 claims description 17
- 230000002159 abnormal effect Effects 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 10
- 238000006073 displacement reaction Methods 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 8
- 230000000877 morphologic effect Effects 0.000 claims description 6
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 8
- 238000004519 manufacturing process Methods 0.000 description 5
- 238000005070 sampling Methods 0.000 description 3
- 238000005034 decoration Methods 0.000 description 2
- 230000032798 delamination Effects 0.000 description 2
- 235000003642 hunger Nutrition 0.000 description 2
- 238000012372 quality testing Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000037351 starvation Effects 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000011031 large-scale manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002310 reflectometry Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The invention provides a line scanning three-dimensional-based ceramic tile defect online detection method, which comprises the following steps: acquiring three-dimensional data corresponding to the ceramic tile; the three-dimensional data is obtained by scanning tiles in a line mode, and the three-dimensional data comprises tile depth data and tile gray scale data; performing image space elevation to object space elevation conversion and section splicing on the three-dimensional data to obtain splicing characteristic data, and performing tile surface extraction on the splicing characteristic data to obtain tile area data; and performing suspected target extraction, suspected target classification and defect target judgment on the tile area data to obtain a tile defect detection result. The online detection method for the ceramic tile defects based on the line scanning three-dimension can solve the problem of poor detection effect of the ceramic tile defects in the prior art and improve the detection effect of the ceramic tile defects.
Description
Technical Field
The invention relates to the technical field of defect detection, in particular to a ceramic tile defect online detection method based on line scanning three-dimension.
Background
The ceramic tile is used as an important floor and wall decoration material and widely applied to the modern building decoration industry, and the appearance, the performance and the service life of the ceramic tile are directly influenced by the quality of the ceramic tile. With the increasing market demand, all large ceramic tile manufacturers step into the stage of large-scale production line production, but in the quality detection link of ceramic tile products, more traditional manual detection means are adopted.
The manual detection not only generates a large amount of labor cost, but also has the problems of low detection speed, difficult complete unification of detection standards and the like; for the ceramic tile quality testing personnel, the ceramic tile quality testing personnel need to work in a strong light environment for a long time, eyes are easy to fatigue, further judgment on the surface defects of the ceramic tiles is influenced, and in addition, adverse effects on the health of workers can also be caused by the severe environment of a production line.
At present, although some researches on automatic detection of tile defects exist in China, the researches are basically based on a two-dimensional image detection technology, detection results are greatly influenced by an image acquisition environment, particularly illumination conditions, and meanwhile, the detection effect on defect types which have little influence on tile reflectivity is poor, so that the detection method can not be basically applied to an actual tile production line.
Disclosure of Invention
The invention provides a line scanning three-dimensional-based ceramic tile defect online detection method, which is used for solving the problem of poor detection effect of ceramic tile defects in the prior art and improving the detection effect of the ceramic tile defects.
The invention provides a line scanning three-dimensional-based ceramic tile defect online detection method, which comprises the following steps:
acquiring three-dimensional data corresponding to the ceramic tile; the three-dimensional data is obtained by scanning tiles in a line mode, and the three-dimensional data comprises tile depth data and tile gray scale data;
performing image space elevation to object space elevation conversion and section splicing on the three-dimensional data to obtain splicing characteristic data, and performing tile surface extraction on the splicing characteristic data to obtain tile area data;
performing suspected target extraction, suspected target classification and defect target judgment on the tile area data to obtain a tile defect detection result;
wherein the suspected target extraction comprises: carrying out binarization processing on the tile depth image and the tile gray level image obtained by converting the tile area data, and extracting a suspected target;
the suspected object classification includes: classifying the suspected target based on the gray scale feature, the size feature and the gray scale contrast feature of the suspected target to obtain a classification result;
the defect target judgment comprises the following steps: and extracting a defect target from the classification result based on the difference characteristic between the tile background texture and the tile defect.
According to the method for detecting the defects of the ceramic tiles on line based on line scanning three-dimension, the method for acquiring the three-dimensional data corresponding to the ceramic tiles comprises the following steps:
controlling a three-dimensional measuring unit to acquire three-dimensional data corresponding to the ceramic tile based on a displacement encoder;
the three-dimensional measuring unit comprises a plurality of sets of three-dimensional measuring sensors, and each set of three-dimensional measuring sensor comprises a three-dimensional camera and a laser.
According to the method for detecting the defects of the ceramic tiles on line scanning three-dimensional basis, the three-dimensional data is subjected to image space elevation to object space elevation conversion and section splicing to obtain splicing characteristic data, and then ceramic tile surface extraction is carried out on the splicing characteristic data to obtain ceramic tile area data, and the method comprises the following steps:
based on the calibration file, the three-dimensional data is converted from the image space elevation to the object space elevation;
splicing the section data in the converted three-dimensional data corresponding to the two adjacent groups of three-dimensional measurement units to obtain splicing characteristic data;
and carrying out tile surface extraction on the splicing characteristic data to obtain tile area data.
According to the online detection method for the ceramic tile defects based on the line scanning three-dimension, provided by the invention, the ceramic tile surface extraction is carried out on the splicing characteristic data to obtain the ceramic tile area data, and the method comprises the following steps:
and based on the elevation value of the tile surface and the characteristic that a gap exists between adjacent tiles, matching the splicing characteristic data, performing tile surface extraction, and obtaining tile area data.
According to the tile defect online detection method based on line scanning three-dimension provided by the invention, the suspected target extraction, the suspected target classification and the defect target judgment are carried out on the tile area data to obtain a tile defect detection result, and the method comprises the following steps:
firstly, removing isolated abnormal values from the tile area data in a filtering mode, and then converting the tile area data from which the isolated abnormal values are removed to obtain a tile depth image and a tile gray level image;
carrying out binarization processing on the tile depth image and the tile gray level image by adopting a global threshold or local self-adaptive threshold method, and extracting a suspected target;
based on the gray scale feature, the size feature and the gray scale contrast feature of the suspected target, carrying out self-adaptive classification on the suspected target by using a clustering algorithm to obtain a classification result;
extracting a defect target from the classification result based on the difference characteristics between the tile background texture and the tile defect;
and combining the defect target corresponding to the tile depth image with the defect target corresponding to the tile gray level image to obtain the tile defect detection result.
According to the tile defect online detection method based on line scanning three-dimension provided by the invention, the suspected target is subjected to self-adaptive classification by using a clustering algorithm based on the gray scale feature, the size feature and the gray scale contrast feature of the suspected target to obtain a classification result, and the method comprises the following steps:
for the suspected target, taking the connected regions as statistical units, and carrying out statistics on gray scale features, size features and gray scale contrast features one by one;
and carrying out self-adaptive classification on each connected region by using a clustering algorithm based on the similarity of the gray scale feature, the size feature and the gray scale contrast feature of each connected region in the suspected target to obtain a classification result.
According to the method for detecting the ceramic tile defects on line based on line scanning three-dimension provided by the invention, the defect target is extracted from the classification result based on the difference characteristics between the ceramic tile background textures and the ceramic tile defects, and the method comprises the following steps:
calculating class characteristics corresponding to the classification results class by class based on the classification results; the class features include: at least one of a representative gray scale feature, a representative size feature, a representative gray scale contrast feature, a connected region total area feature, a gray scale consistency feature, a size consistency feature, a gray scale contrast consistency feature, and an internal comprehensive similarity feature;
calculating the difference characteristic value between any two classes of classification results based on the class characteristics corresponding to the classification results;
calculating the total characteristic value of the difference between the self class and all other classes one by one based on the difference characteristic value between the classes of any two classes of classification results;
determining the class where the potential defect target is located based on the total difference characteristic value between the self class and all other classes and the total area characteristic of the connected region of the self class;
and extracting the defect target by utilizing the difference characteristics between the tile background texture and the tile defect for all the connected areas in the class where the potential defect target is located.
According to the line scanning three-dimensional-based tile defect online detection method provided by the invention, the inter-class difference characteristic values comprise: at least one of representative gray scale feature difference, representative size feature difference, representative gray scale contrast feature difference, connected region total area feature difference, gray scale consistency feature difference, size consistency feature difference, gray scale contrast consistency feature difference and internal comprehensive similarity feature difference.
The ceramic tile defect online detection method based on line scanning three-dimension provided by the invention further comprises the following steps:
classifying the tile defect detection results based on the expression characteristics of the tile defect detection results in the tile depth data and the tile gray scale data, and counting the occurrence frequency of various tile defects;
the expression characteristics comprise depth deviation numerical characteristics, gray deviation degree numerical characteristics and morphological characteristics of the defect target.
The invention provides a line scanning three-dimensional-based tile defect online detection method, which comprises the steps of carrying out line scanning on a tile to obtain three-dimensional data corresponding to the tile, namely tile depth data and tile gray data, carrying out image-to-object conversion, section splicing and tile surface extraction on the tile data, and then carrying out suspected target extraction, suspected target classification and defect target judgment to obtain a tile defect detection result. The reliability of the ceramic tile defect detection result can be improved by combining the ceramic tile depth data and the ceramic tile gray scale data. And moreover, the three-dimensional data corresponding to the ceramic tiles are obtained in a line scanning mode, the method has the advantage of high data acquisition speed, and the real-time online detection of the ceramic tile defects on the production line can be realized.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of the online detection method for ceramic tile defects based on line scanning three-dimension provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for detecting the ceramic tile defect on line based on line scanning three-dimension of the invention is described in the following with reference to fig. 1.
As shown in fig. 1, the present invention provides a method for detecting defects of ceramic tiles on line based on line scanning three-dimension, comprising:
It can be understood that three-dimensional data acquisition device fixed mounting is in the conveyer belt top, and the ceramic tile is located the transportation conveyer belt, through the removal of transportation conveyer belt, drives the ceramic tile and removes, and when the ceramic tile removed, three-dimensional data acquisition device carried out line scanning to the ceramic tile, acquireed ceramic tile three-dimensional data. The three-dimensional data includes depth data and gray scale data of the tile.
And 120, performing image space elevation to object space elevation conversion and section splicing on the three-dimensional data to obtain splicing characteristic data, and performing tile surface extraction on the splicing characteristic data to obtain tile area data.
It can be understood that the preliminary processing of the three-dimensional data includes image space elevation to object space elevation conversion, section stitching and tile surface extraction. And the tile surface extraction is to independently extract the depth data and the gray data corresponding to each tile.
The image space elevation is the tile depth data collected by the three-dimensional measuring unit, and the object space elevation is the actual elevation of the tile.
And step 130, performing suspected target extraction, suspected target classification and defect target judgment on the tile area data to obtain a tile defect detection result.
And (3) detecting the defects of the ceramic tiles, wherein the steps of suspected target extraction, suspected target classification and defect target judgment are included. And (3) detecting the ceramic tile defects in the ceramic tile depth data and the ceramic tile gray scale data respectively, and combining detection results in the two data to obtain a final ceramic tile defect detection result.
Wherein the suspected target extraction comprises: firstly, removing an isolated abnormal value from the tile area data in a filtering mode, then converting the tile area data from which the isolated abnormal value is removed to obtain a tile depth image and a tile gray level image, carrying out binarization processing on the tile depth image and the tile gray level image, and extracting a suspected target;
the suspected object classification includes: classifying the suspected target based on the gray scale feature, the size feature and the gray scale contrast feature of the suspected target to obtain a classification result;
the defect target judgment comprises the following steps: and extracting a defect target from the classification result based on the difference characteristic between the tile background texture and the tile defect.
In some embodiments, the obtaining three-dimensional data corresponding to a tile includes:
controlling a three-dimensional measuring unit to acquire three-dimensional data corresponding to the ceramic tile based on a displacement encoder;
the three-dimensional measuring unit comprises a plurality of sets of three-dimensional measuring sensors, and each set of three-dimensional measuring sensor comprises a three-dimensional camera and a laser.
It is understood that line scanning may be achieved based on a three-dimensional data acquisition device comprising a line scanning three-dimensional measurement unit and a high precision displacement encoder.
The line scanning three-dimensional measuring unit consists of a plurality of sets of line scanning three-dimensional measuring sensors and is fixedly arranged on a door-shaped bracket crossing the tile conveying conveyor belt at a certain horizontal interval; the line scanning three-dimensional measuring sensor consists of a high-speed three-dimensional camera and a line laser.
The laser device projects a laser line along the width direction of the conveyor belt, the three-dimensional camera obtains the elevation of the surface of the ceramic tile corresponding to the laser line by using the laser triangulation principle, the line scanning three-dimensional measurement unit can obtain three-dimensional depth data and gray data of the cross section of a single ceramic tile through single measurement, and data acquisition is continuously carried out at equal intervals under the control of the displacement encoder, so that the three-dimensional depth data and the gray data of the surface of the complete ceramic tile can be obtained.
In some embodiments, the performing image space elevation to object space elevation conversion and section splicing on the three-dimensional data to obtain splicing characteristic data, and performing tile surface extraction on the splicing characteristic data to obtain tile area data includes:
based on the calibration file, the three-dimensional data is converted from the image space elevation to the object space elevation;
splicing the section data in the converted three-dimensional data corresponding to the two adjacent groups of three-dimensional measurement units to obtain splicing characteristic data;
and carrying out tile surface extraction on the splicing characteristic data to obtain tile area data.
It can be understood that the preliminary processing of the three-dimensional data includes image space elevation to object space elevation conversion, section stitching and tile surface extraction.
The conversion from the image space elevation to the object space elevation is realized through a calibration file.
And the section splicing comprises the splicing of transverse section data between adjacent three-dimensional measuring sensors and the splicing of continuous section data along the moving direction of the conveyor belt. The section splicing operation comprises splicing of three-dimensional depth data and splicing of gray data.
In some embodiments, the extracting the tile surface from the tile surface characteristic data to obtain the tile area data includes:
and based on the elevation value of the tile surface and the characteristic that a gap exists between adjacent tiles, matching the splicing characteristic data, performing tile surface extraction, and obtaining tile area data.
It can be understood that the tile surface extraction is to locate the area corresponding to the tile from the continuous depth data (i.e. the depth feature data in the splicing feature data) according to the feature that the tile surface elevation value is greater than the plane of the conveyor belt and a gap exists between adjacent tiles, and extract the depth data and the gray data of the area corresponding to each tile separately.
In some embodiments, the performing suspected target extraction, suspected target classification, and defect target determination on the tile area data to obtain a tile defect detection result includes:
firstly, removing isolated abnormal values from the tile area data in a filtering mode, and then converting the tile area data from which the isolated abnormal values are removed to obtain a tile depth image and a tile gray level image;
carrying out binarization processing on the tile depth image and the tile gray level image by adopting a global threshold or local self-adaptive threshold method, and extracting a suspected target;
based on the gray scale feature, the size feature and the gray scale contrast feature of the suspected target, carrying out self-adaptive classification on the suspected target by utilizing a clustering algorithm to obtain a classification result;
extracting a defect target from the classification result based on the difference characteristics between the tile background texture and the tile defect;
and combining the defect target corresponding to the tile depth image with the defect target corresponding to the tile gray level image to obtain the tile defect detection result.
In some embodiments, the adaptively classifying the suspected target based on the gray scale feature, the size feature, and the gray scale contrast feature of the suspected target by using a clustering algorithm to obtain a classification result includes:
for the suspected target, taking the connected regions as statistical units, and carrying out statistics on gray scale features, size features and gray scale contrast features one by one;
and based on the similarity of the gray feature, the size feature and the gray contrast feature of each connected region in the suspected target, carrying out self-adaptive classification on each connected region by using a clustering algorithm to obtain a classification result.
In some embodiments, the extracting a defect target from the classification result based on a difference feature between the tile background texture and the tile defect includes:
calculating class characteristics corresponding to the classification results class by class based on the classification results; the class features include: at least one of a representative gray scale feature, a representative size feature, a representative gray scale contrast feature, a connected region total area feature, a gray scale consistency feature, a size consistency feature, a gray scale contrast consistency feature, and an internal comprehensive similarity feature;
calculating the difference characteristic value between any two classes based on the class characteristics corresponding to the classification result; wherein the inter-class difference characteristic values comprise: at least one of a representative gray scale feature difference, a representative size feature difference, a representative gray scale contrast feature difference, a connected region total area feature difference, a gray scale consistency feature difference, a size consistency feature difference, a gray scale contrast consistency feature difference, and an internal comprehensive similarity feature difference;
calculating the total characteristic value of the difference between the self class and all other classes one by one based on the difference characteristic value between the classes of any two classes of classification results;
determining the class where the potential defect target is located based on the total characteristic value of the difference between the self class and all other classes and the total area characteristic of the connected region of the self class;
and extracting the defect target from all connected regions in the class where the potential defect target is located by using the difference characteristics between the tile background texture and the tile defect.
It can be understood that the representative gray feature difference is the difference of the representative gray features corresponding to the two classification results; representing the size characteristic difference, namely the difference of the representing size characteristics corresponding to the two classification results; representing the difference of the gray scale contrast characteristics, namely representing the difference of the gray scale contrast characteristics corresponding to the two classification results; representing the difference of the gray scale contrast characteristics, namely representing the difference of the gray scale contrast characteristics corresponding to the two classification results; other feature differences are the same.
And (3) detecting the defects of the ceramic tiles, wherein the steps of suspected target extraction, suspected target classification and defect target judgment are included.
The suspected target extraction comprises the steps of firstly removing isolated abnormal values in tile area data in a filtering (for example, median filtering or Gaussian filtering) mode, then converting the tile area data from which the isolated abnormal values are removed to obtain a tile depth image and a tile gray level image, and then carrying out binarization on the tile depth image and the tile gray level image, wherein the binarization comprises a method based on a global threshold value or a local adaptive threshold value, the tile depth image is obtained by converting the tile depth data, and the tile gray level image is obtained by converting the tile gray level data.
And the suspected target classification is to perform self-adaptive classification on the suspected target extracted by the binarization processing by utilizing a clustering algorithm according to the similarity of the gray scale feature, the size feature and the gray scale contrast feature of the target to obtain a classification result.
And defect target judgment, namely calculating class characteristics and total characteristic values of differences between the class and all other classes according to the classification result, determining the class where the potential defect target is located according to the total characteristic values of the differences between the class and all other classes and the total area characteristic of a connected region of the class, judging all the connected regions in the class where the potential defect target is located by using the difference characteristics between the background texture of the ceramic tile and the defect of the ceramic tile, combining the defect target corresponding to the depth image of the ceramic tile and the defect target corresponding to the gray image of the ceramic tile, and finally determining the detection result of the defect of the ceramic tile.
In some embodiments, the online detection method for ceramic tile defects based on line scanning three-dimension further comprises:
classifying the tile defect detection results based on the expression characteristics of the tile defect detection results in the tile depth data and the tile gray scale data, and counting the occurrence frequency of various tile defects;
wherein the expression characteristics comprise depth deviation numerical characteristics, gray deviation numerical characteristics and morphological characteristics of the defect target.
It can be understood that the defect classification and statistics are performed according to the performance characteristics of the detected tile defects in the depth data and the gray data, and the frequency of occurrence of each type of defects is counted.
The depth deviation numerical characteristic of the target defect refers to the size of the depth numerical value of the target defect relative to the tile background, the gray deviation numerical characteristic of the target defect refers to the size of the gray numerical value of the target defect relative to the tile background, and the morphological characteristic of the target defect refers to the size and shape characteristic of the target defect.
The performance characteristics include depth deviation numerical characteristics, gray deviation numerical characteristics, and morphological characteristics of the defect target. Typical defects that can be detected from tile depth data include cracks, missing edges, missing corners, pinholes, dirt, blisters, bubbles, stripping, glaze shrinking, delamination and deformation; the typical defects described above can also be detected from tile gray scale data, but also tile defects that do not have significant changes in elevation, such as ink drips, ink starvation, white edges, and pitted surfaces.
And combining the expression characteristics of the defects in the depth data and the gray data to realize the classification of the defects, and recording the times of various types of defects detected in a period of time.
In other embodiments, the online detection method for the tile defect based on line scanning three-dimension provided by the invention comprises the following steps:
in the embodiment, three-dimensional data corresponding to tiles are acquired based on a line scanning three-dimensional data acquisition device, the line scanning three-dimensional data acquisition device comprises a line scanning three-dimensional measurement unit and a high-precision displacement encoder, wherein the three-dimensional measurement unit consists of 3 sets of line scanning three-dimensional measurement sensors and is fixedly installed on a door-shaped bracket crossing a tile conveying conveyor belt at a certain horizontal interval, and the measurement ranges of adjacent three-dimensional measurement sensors in the measurement width direction have partial overlapping areas; the high-precision displacement encoder is used for controlling the three-dimensional measuring unit to continuously and equidistantly acquire data.
The line scanning three-dimensional measurement sensor comprises a high-speed three-dimensional camera and a line laser, multiple sets of three-dimensional measurement sensors share the same line laser, the laser projects laser lines along the width direction of the conveyor belt, the three-dimensional camera acquires the elevation of the surface of a ceramic tile corresponding to the laser lines by using a laser triangulation principle, a line scanning three-dimensional measurement unit can acquire three-dimensional depth data and gray data of a single cross section through single measurement, and the depth data and the gray data of the surface of the complete ceramic tile can be acquired by continuously and equidistantly collecting the ceramic tiles on the moving conveyor belt under the control of a displacement encoder.
The sampling interval of the line scanning three-dimensional measuring unit in the measuring cross section direction is 0.10mm, the sampling interval in the conveying belt displacement direction is 0.10mm, the measuring precision in the elevation direction is 0.05mm, and the online detection speed is 60 m/min.
The conversion from the image space elevation value to the object space elevation is realized through a calibration file according to the image space elevation data (namely, depth data) acquired by the line scanning three-dimensional measurement unit. And (3) acquiring the overlapping area (30mm) of the adjacent sensors in the section measuring direction by calibrating the section elevation data after the image space is converted into the object space, and calculating the overlapping area (about 300 pixel points) of the measured values of the adjacent sensors in the section direction by combining the sampling intervals of the sensors in the section measuring direction.
Measuring data of overlapping areas of adjacent line scanning three-dimensional measuring sensors, and calculating the average value of the measured data as data after splicing of the overlapping areas; the cross section measured values of all adjacent line scanning three-dimensional measuring sensors are selected to be transversely spliced step by step from left to right to form complete cross section data; and finally, splicing the complete cross section data along the moving direction of the conveyor belt. The above-described data stitching operation needs to be performed also for gradation data.
After the data splicing operation is completed, according to the characteristic that the elevation value of the tile surface is larger than the plane of the conveyor belt and a gap exists between adjacent tiles, the area corresponding to each tile is positioned from continuous depth data, and then the depth data and the gray data corresponding to each tile are extracted independently.
And respectively carrying out defect detection on the tile depth data and the tile gray data, wherein the detection steps comprise suspected target extraction, suspected target classification and defect target judgment.
Firstly, performing 5-by-5 window median filtering on the tile data twice continuously, eliminating discrete isolated abnormal values in the tile data, converting the tile depth data into a depth image and converting the gray data into a gray image after the abnormal values are processed.
And respectively carrying out binarization on the ceramic tile depth image and the gray level image, wherein the adopted binarization method is an adaptive threshold method based on the local neighborhood block mean value.
And for the suspected target extracted by the binarization processing, carrying out self-adaptive classification on the suspected target by using a clustering algorithm according to the similarity of the gray scale feature, the size feature and the gray scale contrast feature of the target to obtain a classification result.
Calculating class characteristics and total difference characteristic values between the class and all other classes according to classification results, determining the class where the potential defect target is located according to the total difference characteristic values between the class and all other classes and the total area characteristics of the connected region of the class, judging all the connected regions in the class where the potential defect target is located by using the difference characteristics between the background texture of the ceramic tile and the defect of the ceramic tile, combining the defect target corresponding to the depth image of the ceramic tile and the defect target corresponding to the gray image of the ceramic tile, and finally determining the detection result of the defect of the ceramic tile.
And classifying the detected ceramic tile defects according to the expression characteristics of the detected ceramic tile defects in the depth data and the gray data, and counting the occurrence frequency of various defects. The performance characteristics include depth deviation numerical characteristics, gray deviation numerical characteristics, and morphological characteristics of the defect target.
Typical defects that can be detected from tile depth data include cracks, missing edges, missing corners, pinholes, dirt, blisters, bubbles, stripping, glaze shrinking, delamination and deformation; the typical defects described above can also be detected from the gray scale data, but also tile defects that do not have significant changes in elevation, such as ink drips, ink starvation, white edges, and pitted surfaces.
And combining the expression characteristics of the defects in the depth data and the gray data to realize the classification of the defects, and recording the times of various types of defects detected in a period of time.
In summary, the online detection method for ceramic tile defects based on line scanning three-dimension provided by the invention comprises the following steps: acquiring three-dimensional data corresponding to the ceramic tile; the three-dimensional data is obtained by scanning tiles in a line mode, and the three-dimensional data comprises tile depth data and tile gray scale data; performing image space elevation to object space elevation conversion and section splicing on the three-dimensional data to obtain splicing characteristic data, and performing tile surface extraction on the splicing characteristic data to obtain tile area data; and performing suspected target extraction, suspected target classification and defect target judgment on the tile area data to obtain a tile defect detection result.
In the method for detecting the ceramic tile defects on line based on line scanning three-dimension provided by the invention, three-dimensional data corresponding to the ceramic tile, namely ceramic tile depth data and ceramic tile gray scale data, are obtained by performing line scanning on the ceramic tile, and after image space to object space conversion, section splicing and ceramic tile surface extraction are performed on the ceramic tile depth data, suspected target extraction, suspected target classification and defect target judgment are performed, so that a ceramic tile defect detection result is obtained. The reliability of the ceramic tile defect detection result can be improved by combining the ceramic tile depth data and the ceramic tile gray scale data. And the three-dimensional data corresponding to the ceramic tiles are acquired in a line scanning mode, so that the method has the advantage of high data acquisition speed, and can realize real-time online detection of the ceramic tile defects on a production line.
Therefore, the online detection method for the ceramic tile defects based on the line scanning three-dimension can solve the problem of poor detection effect of the ceramic tile defects in the prior art and improve the detection effect of the ceramic tile defects.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A tile defect online detection method based on line scanning three-dimension is characterized by comprising the following steps:
acquiring three-dimensional data corresponding to the ceramic tile; the three-dimensional data is obtained by scanning tiles in a line mode, and the three-dimensional data comprises tile depth data and tile gray scale data;
performing image space elevation to object space elevation conversion and section splicing on the three-dimensional data to obtain splicing characteristic data, and performing tile surface extraction on the splicing characteristic data to obtain tile area data;
performing suspected target extraction, suspected target classification and defect target judgment on the tile area data to obtain a tile defect detection result;
wherein the suspected target extraction comprises: carrying out binarization processing on the tile depth image and the tile gray level image obtained by converting the tile area data, and extracting a suspected target;
the suspected object classification includes: classifying the suspected target based on the gray scale feature, the size feature and the gray scale contrast feature of the suspected target to obtain a classification result;
the defect target judgment comprises the following steps: and extracting a defect target from the classification result based on the difference characteristic between the tile background texture and the tile defect.
2. The method for detecting the defect of the ceramic tile on the line based on the line scanning three-dimension as claimed in claim 1, wherein the obtaining the three-dimensional data corresponding to the ceramic tile comprises:
controlling a three-dimensional measuring unit to acquire three-dimensional data corresponding to the ceramic tile based on a displacement encoder;
the three-dimensional measuring unit comprises a plurality of sets of three-dimensional measuring sensors, and each set of three-dimensional measuring sensor comprises a three-dimensional camera and a laser.
3. The method for detecting the defect of the ceramic tile on the line scanning three-dimensional basis according to claim 2, wherein the step of performing image space elevation to object space elevation conversion and section splicing on the three-dimensional data to obtain splicing characteristic data, and performing tile surface extraction on the splicing characteristic data to obtain tile area data comprises the steps of:
based on the calibration file, the three-dimensional data is converted from the image space elevation to the object space elevation;
splicing section data in the converted three-dimensional data corresponding to the two adjacent groups of three-dimensional measurement units to obtain splicing characteristic data;
and carrying out tile surface extraction on the splicing characteristic data to obtain tile area data.
4. The on-line detection method for ceramic tile defects based on line scanning three-dimension as claimed in claim 3, wherein the ceramic tile surface extraction is performed on the splicing characteristic data to obtain the ceramic tile area data, and comprises:
and based on the elevation value of the tile surface and the characteristic that a gap exists between adjacent tiles, matching the splicing characteristic data, performing tile surface extraction, and obtaining tile area data.
5. The method for on-line detection of ceramic tile defects based on line scanning three-dimensional according to any one of claims 1-4, wherein the step of performing suspected target extraction, suspected target classification and defect target judgment on the ceramic tile area data to obtain a ceramic tile defect detection result comprises:
firstly, removing isolated abnormal values from the tile area data in a filtering mode, and then converting the tile area data from which the isolated abnormal values are removed to obtain a tile depth image and a tile gray level image;
carrying out binarization processing on the tile depth image and the tile gray level image by adopting a global threshold or local adaptive threshold method, and extracting a suspected target;
based on the gray scale feature, the size feature and the gray scale contrast feature of the suspected target, carrying out self-adaptive classification on the suspected target by utilizing a clustering algorithm to obtain a classification result;
extracting a defect target from the classification result based on the difference characteristics between the tile background texture and the tile defect;
and combining the defect target corresponding to the tile depth image with the defect target corresponding to the tile gray level image to obtain the tile defect detection result.
6. The method for on-line detection of ceramic tile defects based on line scanning three-dimension according to claim 5, wherein the self-adaptive classification of the suspected target based on the gray scale feature, the size feature and the gray scale contrast feature of the suspected target by using a clustering algorithm to obtain a classification result comprises:
for the suspected target, taking the connected regions as statistical units, and carrying out statistics on gray scale features, size features and gray scale contrast features one by one;
and based on the similarity of the gray feature, the size feature and the gray contrast feature of each connected region in the suspected target, carrying out self-adaptive classification on each connected region by using a clustering algorithm to obtain a classification result.
7. The method for detecting the defect of the ceramic tile on the line scanning three-dimensional basis according to claim 5, wherein the step of extracting the defect target from the classification result based on the difference characteristic between the background texture of the ceramic tile and the defect of the ceramic tile comprises the following steps:
calculating class characteristics corresponding to the classification results class by class based on the classification results; the class features include: at least one of a representative gray scale feature, a representative size feature, a representative gray scale contrast feature, a connected region total area feature, a gray scale consistency feature, a size consistency feature, a gray scale contrast consistency feature, and an internal comprehensive similarity feature;
calculating the difference characteristic value between any two classes of classification results based on the class characteristics corresponding to the classification results;
calculating the total characteristic value of the difference between the self class and all other classes one by one based on the difference characteristic value between the classes of any two classes of classification results;
determining the class where the potential defect target is located based on the total difference characteristic value between the self class and all other classes and the total area characteristic of the connected region of the self class;
and extracting the defect target from all connected regions in the class where the potential defect target is located by using the difference characteristics between the tile background texture and the tile defect.
8. The method for on-line detection of defects of ceramic tiles based on line scanning three-dimensional according to claim 5, wherein the inter-class difference characteristic values comprise: at least one of representative gray scale feature difference, representative size feature difference, representative gray scale contrast feature difference, connected region total area feature difference, gray scale consistency feature difference, size consistency feature difference, gray scale contrast consistency feature difference and internal comprehensive similarity feature difference.
9. The line scanning three-dimensional-based tile defect online detection method according to claim 5, further comprising:
classifying the tile defect detection results based on the expression characteristics of the tile defect detection results in the tile depth data and the tile gray scale data, and counting the occurrence frequency of various tile defects;
the expression characteristics comprise depth deviation numerical characteristics, gray deviation degree numerical characteristics and morphological characteristics of the defect target.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111651839.8A CN114486732B (en) | 2021-12-30 | 2021-12-30 | Ceramic tile defect online detection method based on line scanning three-dimension |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111651839.8A CN114486732B (en) | 2021-12-30 | 2021-12-30 | Ceramic tile defect online detection method based on line scanning three-dimension |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114486732A true CN114486732A (en) | 2022-05-13 |
| CN114486732B CN114486732B (en) | 2024-04-09 |
Family
ID=81497362
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111651839.8A Active CN114486732B (en) | 2021-12-30 | 2021-12-30 | Ceramic tile defect online detection method based on line scanning three-dimension |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114486732B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115078384A (en) * | 2022-06-16 | 2022-09-20 | 华侨大学 | Quick detection device of stone material large panel surface pit and crack |
| CN117723547A (en) * | 2023-12-13 | 2024-03-19 | 江苏嘉耐高温材料股份有限公司 | Quality monitoring system and method for finished product preparation based on magnesia carbon bricks |
| CN117974666A (en) * | 2024-04-01 | 2024-05-03 | 陕西合阳风动工具有限责任公司 | Quality anomaly detection method for non-circular planetary gear |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101871895A (en) * | 2010-05-10 | 2010-10-27 | 重庆大学 | Laser scanning imaging nondestructive inspection method for hot continuous casting blank surface defects |
| CN102288613A (en) * | 2011-05-11 | 2011-12-21 | 北京科技大学 | Surface defect detecting method for fusing grey and depth information |
| CN104458755A (en) * | 2014-11-26 | 2015-03-25 | 吴晓军 | Multi-type material surface defect detection method based on machine vision |
| CN104599283A (en) * | 2015-02-10 | 2015-05-06 | 南京林业大学 | Image depth improvement method for camera height recovery based on depth difference |
| CN104749187A (en) * | 2015-03-25 | 2015-07-01 | 武汉武大卓越科技有限责任公司 | Tunnel lining disease detection device based on infrared temperature field and gray level image |
| US20170169605A1 (en) * | 2015-12-10 | 2017-06-15 | Ocean Networks Canada Society | Automated generation of digital elevation models |
| CN107633516A (en) * | 2017-09-21 | 2018-01-26 | 武汉武大卓越科技有限责任公司 | A kind of method and apparatus for identifying surface deformation class disease |
| CN107743421A (en) * | 2015-06-11 | 2018-02-27 | 宝洁公司 | Apparatus and method for composition to be applied to surface |
| CN108319920A (en) * | 2018-02-05 | 2018-07-24 | 武汉武大卓越科技有限责任公司 | A kind of pavement strip detection and calculation method of parameters scanning three-dimensional point cloud based on line |
| CN108490000A (en) * | 2018-03-13 | 2018-09-04 | 北京科技大学 | A kind of Bar Wire Product surface defect on-line measuring device and method |
| CN110473187A (en) * | 2019-08-08 | 2019-11-19 | 武汉武大卓越科技有限责任公司 | A kind of line scanning three-dimensional pavement crack extract method of object-oriented |
| CN110569786A (en) * | 2019-09-06 | 2019-12-13 | 中国农业科学院农业资源与农业区划研究所 | fruit tree identification and quantity monitoring method and system based on unmanned aerial vehicle data acquisition |
| CN112348773A (en) * | 2020-09-28 | 2021-02-09 | 歌尔股份有限公司 | Screen defect detection method and device and electronic equipment |
| CN113129265A (en) * | 2021-03-18 | 2021-07-16 | 广东工业大学 | Method and device for detecting surface defects of ceramic tiles and storage medium |
| US11153503B1 (en) * | 2018-04-26 | 2021-10-19 | AI Incorporated | Method and apparatus for overexposing images captured by drones |
| CN113701678A (en) * | 2021-09-18 | 2021-11-26 | 武汉光谷卓越科技股份有限公司 | Road surface flatness detection method based on line scanning three-dimension |
| CN113847884A (en) * | 2021-09-18 | 2021-12-28 | 武汉光谷卓越科技股份有限公司 | Fine three-dimensional measurement and modeling method based on line scanning |
-
2021
- 2021-12-30 CN CN202111651839.8A patent/CN114486732B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101871895A (en) * | 2010-05-10 | 2010-10-27 | 重庆大学 | Laser scanning imaging nondestructive inspection method for hot continuous casting blank surface defects |
| CN102288613A (en) * | 2011-05-11 | 2011-12-21 | 北京科技大学 | Surface defect detecting method for fusing grey and depth information |
| CN104458755A (en) * | 2014-11-26 | 2015-03-25 | 吴晓军 | Multi-type material surface defect detection method based on machine vision |
| CN104599283A (en) * | 2015-02-10 | 2015-05-06 | 南京林业大学 | Image depth improvement method for camera height recovery based on depth difference |
| CN104749187A (en) * | 2015-03-25 | 2015-07-01 | 武汉武大卓越科技有限责任公司 | Tunnel lining disease detection device based on infrared temperature field and gray level image |
| CN107743421A (en) * | 2015-06-11 | 2018-02-27 | 宝洁公司 | Apparatus and method for composition to be applied to surface |
| US20170169605A1 (en) * | 2015-12-10 | 2017-06-15 | Ocean Networks Canada Society | Automated generation of digital elevation models |
| CN107633516A (en) * | 2017-09-21 | 2018-01-26 | 武汉武大卓越科技有限责任公司 | A kind of method and apparatus for identifying surface deformation class disease |
| CN108319920A (en) * | 2018-02-05 | 2018-07-24 | 武汉武大卓越科技有限责任公司 | A kind of pavement strip detection and calculation method of parameters scanning three-dimensional point cloud based on line |
| CN108490000A (en) * | 2018-03-13 | 2018-09-04 | 北京科技大学 | A kind of Bar Wire Product surface defect on-line measuring device and method |
| US11153503B1 (en) * | 2018-04-26 | 2021-10-19 | AI Incorporated | Method and apparatus for overexposing images captured by drones |
| CN110473187A (en) * | 2019-08-08 | 2019-11-19 | 武汉武大卓越科技有限责任公司 | A kind of line scanning three-dimensional pavement crack extract method of object-oriented |
| CN110569786A (en) * | 2019-09-06 | 2019-12-13 | 中国农业科学院农业资源与农业区划研究所 | fruit tree identification and quantity monitoring method and system based on unmanned aerial vehicle data acquisition |
| CN112348773A (en) * | 2020-09-28 | 2021-02-09 | 歌尔股份有限公司 | Screen defect detection method and device and electronic equipment |
| CN113129265A (en) * | 2021-03-18 | 2021-07-16 | 广东工业大学 | Method and device for detecting surface defects of ceramic tiles and storage medium |
| CN113701678A (en) * | 2021-09-18 | 2021-11-26 | 武汉光谷卓越科技股份有限公司 | Road surface flatness detection method based on line scanning three-dimension |
| CN113847884A (en) * | 2021-09-18 | 2021-12-28 | 武汉光谷卓越科技股份有限公司 | Fine three-dimensional measurement and modeling method based on line scanning |
Non-Patent Citations (5)
| Title |
|---|
| ANDRZEJ SIOMA: "Automated Control of Surface Defects on Ceramic Tiles Using 3D Image Analysis", MATERIALS, vol. 13, no. 5, pages 1 - 13 * |
| TAO, JIAYUAN 等: "Defect detection for end surface of ferrite magnetic tile", PROCEEDINGS OF SPIE, pages 1 - 9 * |
| 吴冰;成文俊;马贺敏;: "基于神经区域生长瓷砖表面缺陷检测", 机电工程技术, no. 12, pages 79 - 105 * |
| 李清泉;邹勤;张德津;: "利用高精度三维测量技术进行路面破损检测", 武汉大学学报(信息科学版), no. 11, pages 52 - 67 * |
| 范天海;黄丹平;田建平;于少东;吴志鹏;董娜;: "基于机器视觉元件管脚高度检测***研究", 光学技术, no. 01, pages 105 - 112 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115078384A (en) * | 2022-06-16 | 2022-09-20 | 华侨大学 | Quick detection device of stone material large panel surface pit and crack |
| CN117723547A (en) * | 2023-12-13 | 2024-03-19 | 江苏嘉耐高温材料股份有限公司 | Quality monitoring system and method for finished product preparation based on magnesia carbon bricks |
| CN117974666A (en) * | 2024-04-01 | 2024-05-03 | 陕西合阳风动工具有限责任公司 | Quality anomaly detection method for non-circular planetary gear |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114486732B (en) | 2024-04-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114486732B (en) | Ceramic tile defect online detection method based on line scanning three-dimension | |
| Elbehiery et al. | Surface defects detection for ceramic tiles using image processing and morphological techniques | |
| CN104101600B (en) | Cross Section of CC Billet testing of small cracks method | |
| CN109870459B (en) | Track slab crack detection method for ballastless track | |
| CN108007364A (en) | A kind of tile detection device and detection method based on RGB-D cameras | |
| CN107490582B (en) | Assembly line workpiece detection system | |
| CN115375686B (en) | Glass edge flaw detection method based on image processing | |
| Samarawickrama et al. | Matlab based automated surface defect detection system for ceremic tiles using image processing | |
| CN104458764B (en) | Curved uneven surface defect identification method based on large-field-depth stripped image projection | |
| CN114581805A (en) | Coating roller surface defect detection method adopting 3D line laser profile technology | |
| CN111539927A (en) | Detection process and algorithm of automobile plastic assembly fastening buckle lack-assembly detection device | |
| CN114119483A (en) | Image processing technology-based quality detection method and device for light wallboard for building | |
| CN105184792B (en) | A kind of saw blade wear extent On-line Measuring Method | |
| CN113643276A (en) | Textile texture defect automatic detection method based on statistical analysis | |
| CN109622404B (en) | Automatic sorting system and method for micro-workpieces based on machine vision | |
| CN111968079A (en) | Three-dimensional pavement crack extraction method based on section local extreme value and segmentation sparsity | |
| CN113720914B (en) | Ultrasonic flaw detection system and ultrasonic flaw detection method | |
| CN114596301A (en) | Coating roller surface defect detection system adopting 3D line laser profile technology | |
| CN109701890A (en) | Magnetic tile surface defect detection and method for sorting | |
| CN110516725B (en) | Machine vision-based wood board stripe spacing and color detection method | |
| Hu et al. | Heavy rail surface defects detection based on the morphology of multi-scale and dual-structure elements | |
| CN111815575A (en) | Bearing steel ball part detection method based on machine vision | |
| CN115014142A (en) | Steel tape scale error measuring method based on machine vision | |
| CN110021027B (en) | Edge cutting point calculation method based on binocular vision | |
| CN111412834A (en) | Tobacco bale paper indentation data detection system and detection method thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |