CN104359925A - System for realizing automatic detection of electronic glass defects - Google Patents
System for realizing automatic detection of electronic glass defects Download PDFInfo
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- CN104359925A CN104359925A CN201410652308.4A CN201410652308A CN104359925A CN 104359925 A CN104359925 A CN 104359925A CN 201410652308 A CN201410652308 A CN 201410652308A CN 104359925 A CN104359925 A CN 104359925A
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Abstract
The invention relates to a system for realizing automatic detection of electronic glass defects. The system comprises a photographing module, a defect detecting module and a display module, wherein the photographing module is used for acquiring an image of electronic glass; the defect detecting module is used for analyzing the image of the electronic glass to obtain a plurality of divided objects and treating each divided object by a defect location process and a defect classification process to obtain defect type of each divided object; the display module is used for displaying the information of the defect type of each divided object. By adopting the system for realizing the automatic detection of the electronic glass defects, the detection efficiency for the electronic glass is improved, waste of labor is reduced, the detection cost is lowered, the detection accuracy and the glass quality are improved, automation degree of an enterprise is increased, large-scale popularization of an automatic glass detection system is facilitated, and the system has a relatively wide application range.
Description
Technical field
The present invention relates to visual pattern detection field, particularly relate to electronic glass quality testing field, specifically refer to a kind of system realizing electronic glass defect and automatically detect.
Background technology
Machine vision replaces human eye measure and judge with machine exactly.Vision Builder for Automated Inspection refers to that will be ingested target by machine vision product converts picture signal to, sends special image processing system to, according to pixel distribution and the information such as brightness, color, is transformed into digitized signal; Picture system carries out various computing to extract clarification of objective to these signals, and then controls on-the-spot device action according to the result differentiated.
Machine vision is a complex art, comprising digital image processing techniques, mechanical engineering technology, control technology, electric source lighting technology, optical image technology, sensor technology, simulation and digital video technology, computer hardware technique, human-machine interface technology etc.These technology are coordinations in machine vision, mutually coordinate application and could form a successful machine vision applications system.
In recent years, along with market increasing rapidly glass product demand, no matter the production of glass product is from quality, kind, or production technology all there occurs the change of matter.The particularly development of present production technology, the quality requirements of high-end product to glass substrate is more and more higher, especially electronic glass, and therefore general warranty glass quality improves its grade and just seems and be even more important.
At present, glass defect detection mainly utilizes artificial on-line checkingi, artificial accuracy of detection is about 0.2mm, and loss is about 3%, and manual detection is subject to the impact of testing staff's subjective factor, easily glass defect is caused undetected, especially the less defect that distorts is undetected, and the easy visual fatigue of workman, is especially on night shift, stability is not high, and human cost is large.
Summary of the invention
The object of the invention is the shortcoming overcoming above-mentioned prior art, provide and a kind ofly utilize the tens of defects eigenwert that neural network classification algorithm computed segmentation object is corresponding, and obtain according to defect characteristic value the defect kind that each cutting object exists, the system that electronic glass defect that what the simple and accuracy of detection of structure was high realize detects automatically.
To achieve these goals, of the present inventionly realize the system that electronic glass defect detects automatically there is following formation:
This realizes the system that electronic glass defect detects automatically, and its principal feature is, described system comprises:
Camera module, in order to obtain the image of described electronic glass;
Defects detection module, in order to obtain several cutting object by the image of electronic glass described in analyzing, and carries out to each cutting object the defect kind that defect location process and classification of defects process obtain each cutting object;
Display module, in order to show the information of the defect kind of each cutting object.
Further, described defects detection module comprises classification of defects unit, described classification of defects unit is in order to calculate each described cutting object by neural network classification algorithm, and obtain tens of defects eigenwert corresponding to each cutting object, and the tens of defects eigenwert corresponding according to each cutting object obtains the defect kind that each cutting object exists.
Further, described defects detection module also comprises Algorithm Learning unit and test detecting unit, wherein:
Described Algorithm Learning unit, in order to carry out the study of neural network classification algorithm by training sample;
Described test detecting unit, in order to be tested described Algorithm Learning unit by test sample book, and the Algorithm Learning unit described in being judged by compare test threshold value and test result is the need of continue studying.
Further, the study of described neural network classification algorithm comprises the study of neural network structure and is connected the study of weights.
Wherein, described defect characteristic value comprises girth eigenwert, area features value, compactness eigenwert, gray scale intermediate value eigenwert, gray average eigenwert, maximum gray scale eigenwert and minimal gray level eigenwert.
Further, described system also comprises:
Light source supplying module, in order to penetrate illumination on electronic glass.
Further, described defects detection module comprises defect location unit, wherein:
Described brightness judging unit, in order to filter out described electronic glass image on brightness lower than or higher than the region of segmentation threshold;
Described object segmentation unit, in order to the brightness that filters out according to described brightness judging unit lower than or higher than segmentation threshold region segmentation described in the image of electronic glass, and obtain described several cutting objects.
Further, described system also comprises framework, computing machine and image pick-up card, described camera module is arranged at framework inside upper part, described image pick-up card is arranged in described computing machine, described display module is connected with described computing machine, described computing machine is fixed on described base of frame, described light source supplying module to be fixed on described framework and to be positioned at the below of the detection position of described electronic glass, and the direction of described light source supplying module illumination is corresponding with the direction that described camera module gathers image.
Further, the top of described computing machine is provided with the first aluminium-plastic panel, the top of described framework is provided with the second aluminium-plastic panel, the arranged outside of the framework between the first described aluminium-plastic panel and the second aluminium-plastic panel has the 3rd aluminium-plastic panel, and the 3rd described aluminium-plastic panel is connected with the first described aluminium-plastic panel and the second aluminium-plastic panel formation hermetically-sealed construction respectively
Wherein, the first described aluminium-plastic panel, the second aluminium-plastic panel and the 3rd aluminium-plastic panel are black aluminium-plastic panel, described system also comprises air purifier, described air purifier is arranged on the top of described framework, described light source supplying module is LED light source, and described camera model is the line-scan digital camera of 16K.
Have employed the system realizing electronic glass defect and automatically detect of the present invention, accuracy of detection can reach 0.05mm, loss, within 0.5%, is obviously better than desk checking, improves the detection efficiency of electronic glass, reduce labor waste, reduce testing cost, improve accuracy of detection and glass quality, strengthen the automaticity of enterprise, be conducive to the large-scale promotion of glass automatic checkout system, there is range of application more widely.
Accompanying drawing explanation
Fig. 1 is the structured flowchart realizing the system that electronic glass defect detects automatically of the present invention.
Fig. 2 is the system schematic perspective view in actual applications realizing electronic glass defect and automatically detect of the present invention.
Fig. 3 is the system front elevation in actual applications realizing electronic glass defect and automatically detect of the present invention.
Embodiment
In order to more clearly describe technology contents of the present invention, conduct further description below in conjunction with specific embodiment.
Refer to Fig. 1, in one embodiment, of the present inventionly realize the system that electronic glass defect detects automatically and comprise:
Camera module, in order to obtain the image of described electronic glass;
Defects detection module, in order to obtain several cutting object by the image of electronic glass described in analyzing, and carries out to each cutting object the defect kind that defect location process and classification of defects process obtain each cutting object;
Display module, in order to show the information of the defect kind of each cutting object.
In a preferred embodiment, described defects detection module comprises classification of defects unit, described classification of defects unit is in order to calculate each described cutting object by neural network classification algorithm, and obtain tens of defects eigenwert corresponding to each cutting object, and the tens of defects eigenwert corresponding according to each cutting object obtains the defect kind that each cutting object exists.
In the preferred embodiment of one, described defects detection module also comprises Algorithm Learning unit and test detecting unit, wherein:
Described Algorithm Learning unit, in order to carry out the study of neural network classification algorithm by training sample;
Described test detecting unit, in order to be tested described Algorithm Learning unit by test sample book, and the Algorithm Learning unit described in being judged by compare test threshold value and test result is the need of continue studying.
In the preferred embodiment of one, the study of described neural network classification algorithm comprises the study of neural network structure and is connected the study of weights.
Wherein, described defect characteristic value comprises girth eigenwert, area features value, compactness eigenwert, gray scale intermediate value eigenwert, gray average eigenwert, maximum gray scale eigenwert and minimal gray level eigenwert.
In the preferred embodiment of one, described system also comprises:
Light source supplying module, in order to penetrate illumination on electronic glass.
In the preferred embodiment of one, described defects detection module comprises defect location unit, wherein:
Described brightness judging unit, in order to filter out described electronic glass image on brightness lower than or higher than the region of segmentation threshold;
Described object segmentation unit, in order to the brightness that filters out according to described brightness judging unit lower than or higher than segmentation threshold region segmentation described in the image of electronic glass, and obtain described several cutting objects.
In the preferred embodiment of one, described system also comprises framework, computing machine and image pick-up card, described camera module is arranged at framework inside upper part, described image pick-up card is arranged in described computing machine, described display module is connected with described computing machine, described computing machine is fixed on described base of frame, described light source supplying module to be fixed on described framework and to be positioned at the below of the detection position of described electronic glass, and the direction of described light source supplying module illumination is corresponding with the direction that described camera module gathers image.
In the preferred embodiment of one, the top of described computing machine is provided with the first aluminium-plastic panel, the top of described framework is provided with the second aluminium-plastic panel, the arranged outside of the framework between the first described aluminium-plastic panel and the second aluminium-plastic panel has the 3rd aluminium-plastic panel, and the 3rd described aluminium-plastic panel is connected with the first described aluminium-plastic panel and the second aluminium-plastic panel formation hermetically-sealed construction respectively
Wherein, the first described aluminium-plastic panel, the second aluminium-plastic panel and the 3rd aluminium-plastic panel are black aluminium-plastic panel, described system also comprises air purifier, described air purifier is arranged on the top of described framework, described light source supplying module is LED light source, and described camera model is the line-scan digital camera of 16K.
Refer to Fig. 2 and Fig. 3, in actual applications, electronic glass defect automatic checkout system of the present invention comprises integrated frame, air purifier, light source, camera, computing machine, image pick-up card, defects detection software, output signal device.
Wherein, described a whole set of detection system is arranged on the production roller-way of electronic glass, the integrated frame producing roller-way top adopts the north plate sealing of black aluminium, and image pick-up card is as the term suggests be gather image, and the image of camera shooting transfers to calculator memory by image pick-up card; Defects detection module is software simulating, and display module is also software simulating, namely outputs signal device.
In the preferred embodiment of one, a whole set of detection system can also comprise signaling module, this signaling module is connected with computing machine, instruct workman to carry out glass lower piece and (from product line, according to signal prompt, glass is divided into certified products or defective products, certified products represents on glass does not have defect, and defective products represents glass defectiveness).
In addition, described air purifier is arranged on integrated frame top, because integral structure adopts the totally-enclosed technology of the north plate of black aluminium, prevent ambient light interference, reduce the noise of image, ensure picture quality, add that the effect that air purifier purifies air ensures that equipment is in the environment of high-cleanness, high from top to bottom, guarantee that camera adopts picture environment within the scope of certain cleanliness factor, prevent dust from disturbing classification of defects, cause and pick up by mistake; Described light source is fixed on by web member on the section bar in integrated frame, is positioned at the top producing roller-way bottom Computer cabinet, and producing roller-way just has a hole identical with light source length and width to the position of light source; Described camera is fixed on camera holder, and be positioned at the top producing roller-way, be arranged on the section bar of middle and upper part in integrated frame, the field range of camera is just to light source; Described computing machine is placed in Computer cabinet, and Computer cabinet is arranged on bottom integrated frame, and display is arranged on the display screen hanging seat of frame right, and keyboard is placed on the keyboard base of frame right.This integral structure framed structure is simply firm, safe and reliable, facilitates the adjustment of camera and light source.
In order to improve the precision of detection further, detection system can adopt the line-scan digital camera of two 16K, red light source, polishing mode adopts backlight, pixel resolution 0.013mm, and camera calibration precision is 0.05 millimeter, human eye accuracy of detection only have about 0.2mm, apparently higher than desk checking.
Because integral device is arranged on the production line of electronic glass, when glass is about to enter camera fields of view, photoelectric sensor senses glass, transmission of signal is to image pick-up card, image acquisition card control camera starts to adopt picture, image frame by frame pass to board from camera, then be delivered to calculator memory from board, computing machine starts analyzing and processing image frame by frame, carries out classification of defects and statistics.
Wherein, if electronic glass existing defects, glass there will be bubble, scratch, the various defects such as tin point, feature according to often kind of defect can find, bubble is ellipse, scuffing is an elongated line, in brightness, larger difference (very bright or very dark) is there is in the tin point defect on image compared to glass zero defect part, be easy to distinguish, other classification of defects is also same, so set up appropriate defect storehouse to often kind of defect, filter out the eigenwert that there is notable difference with other defect, computing machine adopts neural network classification algorithm, neural network classification algorithm comprises the study of neural network structure and is connected the study of weights, particular content is:
Early stage, often kind of defect prepared some samples, and pick-up unit carries out the study of neural network by training sample; Pick-up unit is tested by test sample book, and judges whether test result reaches test threshold; Judged result is the test threshold that described test result reaches described, if do not reach requirement to continue test study.
This senior defect classification method is higher than general sorting algorithm success ratio, constantly gathers defective data, be filled in defect storehouse in the process of producing.
After electronic glass leaves photoelectric sensor, photoelectric sensor transmission of signal is to image pick-up card, image acquisition card control camera stops adopting picture, defects detection software is according to the defect classification of monolithic glass, the condition judgment Current electronic glass such as defects count are certified products or unacceptable product, pilot lamp is exported to by I/O module afterwards, instruction operative employee how bottom sheet by glass grades signal.
The present invention can be used for the online defects detection of electronic glass, according to the difference detecting glass size, the parameter that change different size is corresponding, dissimilar electronic glass can be detected, be applicable to the operation of automated production of electronic glass, be specially adapted to continuously that uninterrupted different areas of activity, automated production requirement are high, accuracy of detection requires the operating mode high, stability requirement is high.
Have employed the system realizing electronic glass defect and automatically detect of the present invention, accuracy of detection can reach 0.05mm, loss is within 0.5%, improve the detection efficiency of electronic glass, reduce labor waste, reduce testing cost, improve accuracy of detection and glass quality, strengthen the automaticity of enterprise, be conducive to the large-scale promotion of glass automatic checkout system, there is range of application more widely.
In this description, the present invention is described with reference to its specific embodiment.But, still can make various amendment and conversion obviously and not deviate from the spirit and scope of the present invention.Therefore, instructions and accompanying drawing are regarded in an illustrative, rather than a restrictive.
Claims (12)
1. realize the system that electronic glass defect detects automatically, it is characterized in that, described system comprises:
Camera module, in order to obtain the image of described electronic glass;
Defects detection module, in order to obtain several cutting object by the image of electronic glass described in analyzing, and carries out to each cutting object the defect kind that defect location process and classification of defects process obtain each cutting object;
Display module, in order to show the information of the defect kind of each cutting object.
2. the system realizing electronic glass defect and automatically detect according to claim 1, it is characterized in that, described defects detection module comprises classification of defects unit, described classification of defects unit is in order to calculate each described cutting object by neural network classification algorithm, and obtain tens of defects eigenwert corresponding to each cutting object, and the tens of defects eigenwert corresponding according to each cutting object obtains the defect kind that each cutting object exists.
3. the system realizing electronic glass defect and automatically detect according to claim 2, is characterized in that, described defects detection module also comprises Algorithm Learning unit and test detecting unit, wherein:
Described Algorithm Learning unit, in order to carry out the study of neural network classification algorithm by training sample;
Described test detecting unit, in order to be tested described Algorithm Learning unit by test sample book, and the Algorithm Learning unit described in being judged by compare test threshold value and test result is the need of continue studying.
4. the system realizing electronic glass defect and automatically detect according to claim 3, is characterized in that, the study of described neural network classification algorithm comprises the study of neural network structure and is connected the study of weights.
5. the system realizing electronic glass defect and automatically detect according to any one of claim 1 to 4, it is characterized in that, described defect characteristic value comprises girth eigenwert, area features value, compactness eigenwert, gray scale intermediate value eigenwert, gray average eigenwert, maximum gray scale eigenwert and minimal gray level eigenwert.
6. the system realizing electronic glass defect and automatically detect according to claim 5, it is characterized in that, described system also comprises:
Light source supplying module, in order to penetrate illumination on electronic glass.
7. the system realizing electronic glass defect and automatically detect according to claim 6, is characterized in that, described defects detection module comprises defect location unit, wherein:
Described brightness judging unit, in order to filter out described electronic glass image on brightness lower than or higher than the region of segmentation threshold;
Described object segmentation unit, in order to the brightness that filters out according to described brightness judging unit lower than or higher than segmentation threshold region segmentation described in the image of electronic glass, and obtain described several cutting objects.
8. the system realizing electronic glass defect and automatically detect according to claim 7, it is characterized in that, described system also comprises framework, computing machine and image pick-up card, described camera module is arranged at framework inside upper part, described image pick-up card is arranged in described computing machine, described display module is connected with described computing machine, described computing machine is fixed on described base of frame, described light source supplying module to be fixed on described framework and to be positioned at the below of the detection position of described electronic glass, and the direction of described light source supplying module illumination is corresponding with the direction that described camera module gathers image.
9. the system realizing electronic glass defect and automatically detect according to claim 8, it is characterized in that, the top of described computing machine is provided with the first aluminium-plastic panel, the top of described framework is provided with the second aluminium-plastic panel, the arranged outside of the framework between the first described aluminium-plastic panel and the second aluminium-plastic panel has the 3rd aluminium-plastic panel, and the 3rd described aluminium-plastic panel is connected with the first described aluminium-plastic panel and the second aluminium-plastic panel formation hermetically-sealed construction respectively
10. the system realizing electronic glass defect and automatically detect according to claim 9, is characterized in that, the first described aluminium-plastic panel, the second aluminium-plastic panel and the 3rd aluminium-plastic panel are black aluminium-plastic panel.
11. systems realizing electronic glass defect and automatically detect according to claim 10, it is characterized in that, described system also comprises air purifier, and described air purifier is arranged on the top of described framework.
12. realize the system that electronic glass defect detects automatically according to any one of claim 6 or 11, and it is characterized in that, described light source supplying module is LED light source, and described camera model is the line-scan digital camera of 16K.
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| CN107153072A (en) * | 2017-06-21 | 2017-09-12 | 苏州卡睿知光电科技有限公司 | A kind of eyeglass flaw inspection method and device |
| CN107341792A (en) * | 2017-06-21 | 2017-11-10 | 苏州卡睿知光电科技有限公司 | A kind of method and device for establishing the model for differentiating eyeglass defect type |
| CN109444146A (en) * | 2018-09-17 | 2019-03-08 | 鲁班嫡系机器人（深圳）有限公司 | A kind of defect inspection method, device and the equipment of industrial processes product |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2017008320A1 (en) * | 2015-07-13 | 2017-01-19 | 武汉华星光电技术有限公司 | Method of detecting quality of polysilicon thin film and system utilizing same |
| CN105197559A (en) * | 2015-09-28 | 2015-12-30 | 福耀玻璃（重庆）配件有限公司 | Automatic remote waste marking device for packaging assembly line |
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| CN107341792A (en) * | 2017-06-21 | 2017-11-10 | 苏州卡睿知光电科技有限公司 | A kind of method and device for establishing the model for differentiating eyeglass defect type |
| CN109444146A (en) * | 2018-09-17 | 2019-03-08 | 鲁班嫡系机器人（深圳）有限公司 | A kind of defect inspection method, device and the equipment of industrial processes product |
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