CN107169957A - A kind of glass flaws on-line detecting system and method based on machine vision - Google Patents
A kind of glass flaws on-line detecting system and method based on machine vision Download PDFInfo
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- CN107169957A CN107169957A CN201710295728.5A CN201710295728A CN107169957A CN 107169957 A CN107169957 A CN 107169957A CN 201710295728 A CN201710295728 A CN 201710295728A CN 107169957 A CN107169957 A CN 107169957A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- 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
- G01N21/958—Inspecting transparent materials or objects, e.g. windscreens
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T7/10—Segmentation; Edge detection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T7/136—Segmentation; Edge detection involving thresholding
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
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Abstract
The invention discloses a kind of glass flaws on-line detecting system based on machine vision and method, including image capture module, processor module, power module, memory module, display module etc..Image capture module uses linear array CCD camera, progressively scans glass to be measured, obtains glass flaws image information；Core processor module mainly includes the TMS320DM642 DSP minimum systems of TI companies, for receiving and handling glass image information；It mainly completes the pretreatment of image, and carries out feature extraction to flaw image by specific algorithm, calculates its geometric feature and gray feature value, Classification and Identification is carried out to it according to these characteristic parameters；Power module provides the source of energy for whole system.The major function of memory module is to realize the temporary cache of glass view data；Display module mainly realizes the display of testing result image information by USB interface.The present invention improves the real-time of glass flaws on-line checking, enhances reliability.
Description
Technical field
The present invention relates to glass production manufacture field, the recognition methods of especially a kind of glass flaws based on machine vision
And specification processing system.
Background technology
The civilian plate glass of China's production occupy global the first, and glass total growth is probably five points of Gross World Product
Two.China has 160 Duo Ge float glass producers at present, and 4/5ths of glass throughput is float glass.According to
By advanced glass technology manufacturing technology, the yield and quality of glass increased, but still occur in finished glass
Some flaws, influence the performance and outward appearance of glass, such as cut, bubble, tin point, ink dot, light distortion.In the production pin of glass
During selling, it is necessary to assure the high-quality of glass, high-performance can just strive to obtain market position, therefore glass flaws detection is glass life
Indispensable link in production system, avoids underproof glass from coming into the market with this, more can be to glass unit divisi8 grade.
In the past, because technology is limited, the glass production line speed of service is slow, and artificial detection turns into detection method main at that time.It is adjoint
The development of science and technology, and the demand of glass is constantly lifted, the production line speed of service of glass is accelerated, artificial detection
Limitation highlight, the requirement of production can not be reached.
The shortcoming that artificial detection is present mainly has：Workshop scene dust is more, and noise is big, and workman's detection working environment is disliked
It is bad, it is impossible to which that, directly using range estimation, labor intensity is big；Normal eye it is continual observation moving object for a period of time can dim eyesight,
Eye is swollen to wait uncomfortable, and testing staff can not non-stop run for a long time, it is impossible to ensure ex factory pass rate；And glass flaws human eye is difficult
Accurate to judge, error is big, and error chance is a lot, it is impossible to ensure detection quality, and speed of production also has very big limitation.
Therefore, in glass production processing industry, it is badly in need of one kind and human eye is replaced using electronic system and method to glass
Flaw is measured and differentiated.
The content of the invention
Present invention aims at providing, a kind of Detection results are accurate, stability is strong, quickly and easily based on machine vision
Glass flaws on-line detecting system and method.
To achieve the above object, following technical scheme is employed：System of the present invention includes linear array CCD camera, embedding
Enter formula processor, power module, memory module and display module；
The signal input part connection of linear array CCD camera and embeded processor, linear array CCD camera progressive scan is to be measured
Glass, obtains the image information of glass and inputs embeded processor；The signal output part and display module of embeded processor
It is connected, embeded processor is separately connected with memory module, power module；Embeded processor is obtained to linear array CCD camera
Glass image information pre-processed after, embeded processor by glass information transmit to display module carry out result shows,
Glass information is transmitted to memory module and stored by embeded processor；The power module is used to provide a system to electricity
Power.
Further, the display module application USB interface, including ADV7123KST140 and peripheral circuit.
Further, the embeded processor is TMS320DM642 processors.
A kind of line detecting method of the present invention, comprises the following steps：
Step 1, linear array CCD camera obtains glass image information and transmitted to embeded processor；
Step 2, embeded processor is pre-processed to glass image information first；Then feature extraction algorithm pair is passed through
Glass flaws image carries out feature extraction, calculates its geometric feature and gray feature value, special according to geometric feature and gray scale
Value indicative carries out Classification and Identification to glass image information；
Step 3, embeded processor is stored the glass image information write-in memory module after processing, meanwhile,
Embeded processor, which transmits the glass image information after processing to display module progress result, to be shown.
Wherein, in step 2, the pretreatment of embeded processor includes filtering and noise reduction, Threshold segmentation processing, in Threshold segmentation
After processing, reuse medium filtering and be filtered processing to image, that is, respectively using in once before and after the Threshold segmentation
Value filtering carries out denoising, obtains accurate muting flaw bullet image；Comprise the following steps that：
Step 2-1, image processor receives image information, and image median filter, medium filtering are carried out to the image of acquisition
Can be with Protect edge information information, computation complexity is smaller, there is good inhibiting effect to salt-pepper noise, therefore it is applied to the glass flaw
Defect detecting system.
Step 2-2, after picture noise is removed, to improve recognition efficiency, enters row threshold division.Threshold value is carried out using OTSU
Segmentation；
Step 2-3, to the image information obtained after processing, in order to extract accurate flaw core, after threshold process, then
Secondary use medium filtering is filtered processing to image, that is, is carried out respectively using a medium filtering before and after Threshold segmentation
Denoising, carries out secondary filtering；
Step 2-4, carries out edge extracting processing to glass flaws image using canny operators, makes image boundary clearly demarcated, right
Target area identification, extraction, understand that analysis lays the first stone.
In step 2, the feature extraction algorithm, which is used to obtain, is conducive to the feature of defect classification, including Image Moment Invariants
The extraction of the characteristics of image such as extraction, the extraction of geometric properties and gray scale, using unbiased U-ReliefF feature selecting algorithms.
In step 3, embeded processor digitally generates image information, and R, G, B three primary colors are changed into by converter
Signal and row, field sync signal, signal, into display module, complete the display of testing result image information by cable transmission.
The unbiased U-ReliefF feature selecting algorithms are carried out in feature selecting, and selected five features are：Elongation
Degree, rectangular degree, gray standard deviation, threshold value, circularity.
Compared with prior art, the invention has the advantages that：
1st, the real-time of glass flaws on-line checking is improved, reliability is enhanced.
2nd, ReliefF algorithm evaluation accuracys are improved so that it is more fair that feature weight is evaluated, and solves sample size pair
The influence of feature weight.
Brief description of the drawings
Fig. 1 is the structured flowchart of present system.
Fig. 2 is the overall workflow figure of present system.
Fig. 3 is image processing program flow chart.
Fig. 4 is image preprocessing flow chart.
Fig. 5 is U-ReliefF algorithm flow charts.
Qualitative description figure of each features of Fig. 6 to flaw.
Fig. 7 characteristic value ordering charts.
Embodiment
The present invention will be further described below in conjunction with the accompanying drawings：
As shown in figure 1, present system is mainly made up of three parts, it is image procossing main control part, IMAQ respectively
Part and result display portion.Production line overall construction drawing as indicated with 1, the letter of linear array CCD camera and embeded processor
Number input connection, linear array CCD camera progressively scans glass to be measured, and the image information for obtaining glass simultaneously inputs embedded processing
Device；The signal output part of embeded processor is connected with display module, embeded processor separately with memory module, power module
It is connected；After embeded processor is pre-processed to the glass image information that linear array CCD camera is obtained, embeded processor will
Glass information, which is transmitted to display module progress result, to be shown, and glass information is transmitted to memory module and carried out by embeded processor
Storage；The power module is used to provide a system to electric power.The system is set based on machine vision by image processing techniques
A set of glass flaws detection and identification system are counted, the detecting system obtains flaw image by linear array CCD camera
Data, by transmission line data transfer to master control detecting system；When data reach processor, convert the data into first
Data signal, and flaw image is pre-processed using image processing techniques, obtain clearly reliably image；Secondly, it is right
Flaw image carries out feature extraction, calculates its geometric feature and gray feature, it is divided according to these characteristic parameters
Class is recognized.
IMAQ and image processing module in the system, described image acquisition module include SG-14-04K80 lines
Array camera, Myutron FV5026L-F camera lenses, the red strip sources of LED of 620nm-750nm wavelength, shone using backlight type
Penetrate, and peripheral circuit, pending image is obtained with this.Image processing module using embedded processing systems as core at
Device is managed, the system overall workflow built is as shown in Figure 2.
The DSP programmings of image procossing, by linear array CCD camera collection Lai image information be temporarily present the slow of system
Rush in memory, and view data is transferred in DSP according to the DSP intrinsic clock cycle.Its internal image processing routine
Flow is as shown in Figure 3.Because glass flaws image data amount is big, so system needs extension external mass storage to deposit
Storage, so DSP is accomplished by keeping data communication with external memory storage, realizes the real-time reading, processing and transmission of data.
The glass image gathered on image preprocessing process, production line to it, it is necessary to carry out after image preprocessing
Feature extraction and flaw identification can effectively be carried out.The process of image preprocessing is mainly completion following items task：Image
Filtering, Threshold segmentation, rim detection etc., as shown in Figure 4.
Step one：Image processor receives image information, carries out image median filter to the image of acquisition, medium filtering can
With Protect edge information information well, computation complexity is smaller, there is good inhibiting effect to salt-pepper noise, therefore it is applied to glass
Glass Defect Detection system.
Step 2：After picture noise is removed, to improve recognition efficiency, enter row threshold division.Threshold value is carried out using OTSU
Segmentation.
Step 3：To the image information obtained after processing, in order to extract accurate flaw core, after threshold process, then
Secondary use medium filtering is filtered processing to image.Carried out respectively using a medium filtering namely before and after Threshold segmentation
Denoising, carries out secondary filtering.
Step 4：Edge extracting processing is carried out to glass flaws image using canny operators, makes image boundary clearly demarcated, it is right
Target area identification, extraction, understand that analysis lays the first stone.
Get after glass flaws area-of-interest, target subject is clear and definite, but only extract with good discrimination
The feature of degree, computer just meets with figure and knows thing.So-called feature refers to the common property that interesting image regions possess, and is also meter
The foundation of calculation machine identification.The extraction of unwanted visual characteristic is that from the object space of image glass flaws are mapped into feature space, this
Mapping relations reduce the information content of flaw image, are that follow-up identification classification lays the foundation.
The feature selection approach of suitable glass flaws classification is explored, a kind of feature selecting algorithm model U- of unbiased is proposed
ReliefF.Algorithm flow chart such as Fig. 5.
Step one：The feature extraction of flaw image.The geometric properties of glass flaws are the notable features for detecting target.Generally
In the case of, geometry is counted as a closing, continuous region, and the profile in this region is a curve, therefore figure
As the description of geometric properties is exactly to this closing, continuous region description；These geometric properties of Fig. 6 describe image jointly
Overall distribution, therefore avoid the influence that the local feature of image is brought, there is good tolerance.
Step 2：The feature selecting of flaw image.The target of feature selecting is to find optimal feature subset.Feature selecting energy
The feature of uncorrelated (irrelevant) or redundancy (redundant) is rejected, so as to reach reduction Characteristic Number, model essence is improved
Exactness, reduces the purpose of run time.On the other hand, very positively related feature reduction model is selected, assistance understands that data are produced
Raw process.
Step 3：Relief algorithms are probed into.The core concept of the algorithm is：Distance of the good feature between similar sample
It is smaller, and the distance between inhomogeneous sample is larger；For a multiclass sample, its inter- object distance is discrete by similar sample
Degree influence, the high sample of within-cluster variance, value is at a higher level.And comparing the sample compacted in class, value is in
One relatively low level.Tagsort ability is assessed with desired value, obtained result is partial to the sample class being dominant in quantity.
Step 4：The foundation of U-Relief algorithm models.In practical application, it is impossible to ensure that the sample of each feature class to the greatest extent may be used
Many, the having differences property of quantity of sample of energy.In the case, to improve ReliefF algorithm evaluation accuracys, its algorithm is entered
Row is improved, as shown in Figure 5.
In order to which more popular understanding features described above is extracted and selection step, illustrated with reference to concrete instance：
For actual production scene extracted unwanted visual characteristic (including：Gray standard deviation (1), threshold value (2), circularity
(3), rectangular degree (4), elongation (5), gradient (6), eccentricity (7), length-width ratio (8), compactedness (9), range of extension (10) etc.
Geometric properties), using U-ReliefF algorithms carry out evaluation selection, while with ReliefF algorithm comparisons, verify innovatory algorithm pair
The validity of glass flaws feature selecting.
For proving and comparisom algorithm, the unwanted visual characteristic of said extracted is commented using ReliefF and U-ReliefF algorithms
Valency is selected, and the feature weight sequence for calculating each feature is as shown in Figure 7.
By Fig. 7 it can also be seen that ReliefF algorithms are carried out in feature selecting, preceding four characteristic values are relatively stablized, the 5th spy
Value indicative increases higher, if using amplification more than 20% as threshold point, the 5th later feature is chosen, that is, is tilted
Degree, elongation, rectangular degree, gray standard deviation, threshold value, 6 features of circularity are selected as the foundation of classification；Similarly understand,
The feature of innovatory algorithm selection is elongation, gradient, circularity, threshold value, 5 features of gray standard deviation, ReliefF algorithms
In rectangular degree feature reject, this is due to that this feature tends to the more tin point of sample in training set, to identification tin point play
Positive role, but act on unobvious for general classification, wrong point is even resulted in, therefore rejected in innovatory algorithm.
The feature gone out according to two kinds of algorithms selections, carries out class test to training sample set respectively.It is selected without feature
Classification accuracy is 71.96% when selecting, and accuracy rate is 82.83% after ReliefF feature selectings, is calculated using U-ReliefF
Method accuracy rate is 85.69%.By feature selecting, total classification recognition accuracy improves 13.73%, wherein relatively being improved after improving
Before improve 2.86%, illustrate necessity of the feature selecting to glass flaws Classification and Identification.
Embodiment described above is only that the preferred embodiment of the present invention is described, not to the model of the present invention
Enclose and be defined, on the premise of design spirit of the present invention is not departed from, technical side of the those of ordinary skill in the art to the present invention
In various modifications and improvement that case is made, the protection domain that claims of the present invention determination all should be fallen into.
Claims (8)
1. a kind of glass flaws on-line detecting system based on machine vision, it is characterised in that：The system is taken the photograph including line array CCD
Camera, embeded processor, power module, memory module and display module；
The signal input part connection of linear array CCD camera and embeded processor, linear array CCD camera progressively scans glass to be measured
Glass, obtains the image information of glass and inputs embeded processor；The signal output part of embeded processor and display module phase
Even, embeded processor is separately connected with memory module, power module；Embeded processor is obtained to linear array CCD camera
After glass image information is pre-processed, embeded processor, which transmits glass information to display module progress result, to be shown, embedding
Enter formula processor and transmit glass information to memory module to be stored；The power module is used to provide a system to electric power.
2. a kind of glass flaws on-line detecting system based on machine vision according to claim 1, it is characterised in that：Institute
State display module application USB interface, including ADV7123KST140 and peripheral circuit.
3. a kind of glass flaws on-line detecting system based on machine vision according to claim 1, it is characterised in that：Institute
Embeded processor is stated for TMS320DM642 processors.
4. a kind of glass flaws online test method based on system described in claim 1, it is characterised in that methods described includes
Following steps：
Step 1, linear array CCD camera obtains glass image information and transmitted to embeded processor；
Step 2, embeded processor is pre-processed to glass image information first；Then by feature extraction algorithm to glass
Flaw image carries out feature extraction, calculates its geometric feature and gray feature value, according to geometric feature and gray feature value
Classification and Identification is carried out to glass image information；
Step 3, embeded processor is stored the glass image information write-in memory module after processing, meanwhile, it is embedded
Formula processor, which transmits the glass image information after processing to display module progress result, to be shown.
5. a kind of glass flaws online test method based on machine vision according to claim 4, it is characterised in that：Step
In rapid 2, the pretreatment of embeded processor includes filtering and noise reduction, Threshold segmentation processing, after Threshold segmentation processing, reuses
Medium filtering is filtered processing to image, that is, carries out denoising using a medium filtering respectively before and after Threshold segmentation,
Obtain accurate muting flaw bullet image；Comprise the following steps that：
Step 2-1, image processor receives image information, and image median filter is carried out to the image of acquisition；
Step 2-2, after picture noise is removed, row threshold division is entered using OTSU；
Step 2-3, to the image information obtained after processing, after threshold process, reuses medium filtering and image is filtered
Ripple processing, that is, denoising is carried out using a medium filtering respectively before and after Threshold segmentation, carry out secondary filtering；
Step 2-4, carries out edge extracting processing to glass flaws image using canny operators, makes image boundary clearly demarcated, to target
Region recognition, extraction, understand that analysis lays the first stone.
6. a kind of glass flaws online test method based on machine vision according to claim 4, it is characterised in that：Step
In rapid 2, the feature extraction algorithm is used to obtain the feature for being conducive to defect classification, includes extraction, the geometry of Image Moment Invariants
The extraction of the characteristics of image such as the extraction of feature and gray scale, using unbiased U-ReliefF feature selecting algorithms.
7. a kind of glass flaws online test method based on machine vision according to claim 4, it is characterised in that：Step
In rapid 3, embeded processor digitally generates image information, and R, G, B tricolor signal and row, field are changed into by converter
Synchronizing signal, signal, into display module, completes the display of testing result image information by cable transmission.
8. a kind of glass flaws online test method based on machine vision according to claim 6, it is characterised in that：Institute
State unbiased U-ReliefF feature selecting algorithms to carry out in feature selecting, selected five features are：Elongation, rectangular degree, ash
Spend standard deviation, threshold value, circularity.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107742301A (en) * | 2017-10-25 | 2018-02-27 | 哈尔滨理工大学 | Transmission line of electricity image processing method under complex background based on image classification |
| CN108000239A (en) * | 2017-12-01 | 2018-05-08 | 常州信息职业技术学院 | Digital control processing on-line detecting system |
| CN108596249A (en) * | 2018-04-24 | 2018-09-28 | 苏州晓创光电科技有限公司 | The method and apparatus of image characteristics extraction and classification |
| CN110232681A (en) * | 2019-05-29 | 2019-09-13 | 深圳新视智科技术有限公司 | Fault methods of exhibiting, device, computer equipment and storage medium |
| CN110596126A (en) * | 2018-05-25 | 2019-12-20 | 上海翌视信息技术有限公司 | Sheet glass edge flaw detection method based on image acquisition |
| CN111223080A (en) * | 2020-01-02 | 2020-06-02 | 长江存储科技有限责任公司 | Wafer detection method and device, electronic equipment and storage medium |
| CN116542975A (en) * | 2023-07-05 | 2023-08-04 | 成都数之联科技股份有限公司 | Defect classification method, device, equipment and medium for glass panel |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102305798A (en) * | 2011-08-02 | 2012-01-04 | 上海交通大学 | Method for detecting and classifying glass defects based on machine vision |
| CN105044119A (en) * | 2015-08-27 | 2015-11-11 | 李明英 | Glass flaw classification method based on gray mean value analysis |
| CN105184783A (en) * | 2015-08-27 | 2015-12-23 | 袁芬 | Glass defect type identification system |
-
2017
- 2017-04-28 CN CN201710295728.5A patent/CN107169957A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102305798A (en) * | 2011-08-02 | 2012-01-04 | 上海交通大学 | Method for detecting and classifying glass defects based on machine vision |
| CN105044119A (en) * | 2015-08-27 | 2015-11-11 | 李明英 | Glass flaw classification method based on gray mean value analysis |
| CN105184783A (en) * | 2015-08-27 | 2015-12-23 | 袁芬 | Glass defect type identification system |
Non-Patent Citations (2)
| Title |
|---|
| 吴房胜 等: "基于数字图像处理的玻璃瑕疵检测系统", 《宜宾学院学报》 * |
| 王平顺: "图像处理技术在玻璃缺陷检测中的应用研究", 《中国优秀硕士学位论文全文数据库》 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107742301A (en) * | 2017-10-25 | 2018-02-27 | 哈尔滨理工大学 | Transmission line of electricity image processing method under complex background based on image classification |
| CN107742301B (en) * | 2017-10-25 | 2021-07-30 | 哈尔滨理工大学 | Image classification-based power transmission line image processing method under complex background |
| CN108000239A (en) * | 2017-12-01 | 2018-05-08 | 常州信息职业技术学院 | Digital control processing on-line detecting system |
| CN108596249A (en) * | 2018-04-24 | 2018-09-28 | 苏州晓创光电科技有限公司 | The method and apparatus of image characteristics extraction and classification |
| CN108596249B (en) * | 2018-04-24 | 2022-04-22 | 苏州晓创光电科技有限公司 | Image feature extraction and classification method and device |
| CN110596126A (en) * | 2018-05-25 | 2019-12-20 | 上海翌视信息技术有限公司 | Sheet glass edge flaw detection method based on image acquisition |
| CN110232681A (en) * | 2019-05-29 | 2019-09-13 | 深圳新视智科技术有限公司 | Fault methods of exhibiting, device, computer equipment and storage medium |
| CN111223080A (en) * | 2020-01-02 | 2020-06-02 | 长江存储科技有限责任公司 | Wafer detection method and device, electronic equipment and storage medium |
| CN116542975A (en) * | 2023-07-05 | 2023-08-04 | 成都数之联科技股份有限公司 | Defect classification method, device, equipment and medium for glass panel |
| CN116542975B (en) * | 2023-07-05 | 2023-09-12 | 成都数之联科技股份有限公司 | Defect classification method, device, equipment and medium for glass panel |
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Application publication date: 20170915 |