CN105424723A - Detecting method for defects of display screen module - Google Patents
Detecting method for defects of display screen module Download PDFInfo
- Publication number
- CN105424723A CN105424723A CN201510859990.9A CN201510859990A CN105424723A CN 105424723 A CN105424723 A CN 105424723A CN 201510859990 A CN201510859990 A CN 201510859990A CN 105424723 A CN105424723 A CN 105424723A
- Authority
- CN
- China
- Prior art keywords
- display screen
- screen module
- image
- defect
- prospect
- 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.)
- Pending
Links
Landscapes
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a detecting method for defects of a display screen module. The detecting method comprises the following steps: S1, detecting and recording non-substantial defects on a surface protection film of a display screen module; S2, detecting and recording all found defects when the display screen module is lightened; S3, filtering the non-substantial defects recorded in S1 from all the found defects in S2, so as to obtain the rest parts which are the real defects of the display screen module. According to the detecting method, non-substantial defects such as bubbles, scratches, dirties and the like on the protection film of a product can be filtered to prevent false alarm, thereby improving the accuracy of detection for the defects of the display screen module.
Description
Technical field
The present invention relates to display technique field, particularly relate to a kind of display screen module defect inspection method.
Background technology
In the defect inspection process of display screen module; bubble on product protection film, cut, the defect such as dirty are not the substantive defects of display screen module; and in traditional detection technique; detect camera above-mentioned unsubstantiality defect to be filtered; but this kind of unsubstantiality defect is carried out Taking Pictures recording; give the alarm, assert that the display screen module of current detection is defective products.Visible, this kind of traditional detection technique owing to the unsubstantiality defect in display screen module can not be filtered, thus causes the generation of false-alarm phenomenon, causes trouble, reduce the accuracy of detection to the defects detection of display screen module.
Summary of the invention
The object of the invention is to overcome weak point of the prior art, a kind of accuracy improving detection is provided, the display screen module defect inspection method preventing false-alarm phenomenon from occurring.
The object of the invention is to be achieved through the following technical solutions:
A kind of display screen module defect inspection method, comprises the steps:
Step S1, detects and records the unsubstantiality defect on display screen module surface protection film;
Step S2, detects and records display screen module under illuminating state, all defect that can find;
Step S3, in all defect that step S2 finds, the unsubstantiality defect recorded in filtering step S1, namely remainder is the real defect of display screen module.
Wherein in an embodiment, step S1 comprises:
Step S11, when display screen module is under not illuminating state or when being switched to black picture, lights the polishing light source of display screen module outside, and the image under obtaining current state;
Step S12, carries out binaryzation to the image in step S11, obtains bianry image, and wherein high light tone area part is the unsubstantiality defect of display screen module surface protection film.
Wherein in an embodiment, step S2 comprises:
Step S21, when display screen module is switched to light tone picture, closes the polishing light source of display screen module outside, and the image under obtaining current state;
Step S22, carries out binaryzation to the image in step S21, obtains bianry image.
Wherein in an embodiment, step S3 comprises:
Step S31, deducts the bianry image that step S12 obtains by the bianry image obtained in step S22, obtain a new bianry image, the real defect being display screen module of its high light tone region display;
Step3Image(i,j)＝Step2Image(i,j)–Step1Image(i,j)，
Step3Image (i, j) in formula, Step2Image (i, j), Step1Image (i, j) represent step S31 respectively, step S22, and the brightness value of the pixel of the i-th row jth row in the bianry image that obtains of step S12;
Step S32, the bianry image that step S31 obtains searches connected region and is real defect.
Wherein in an embodiment, the method obtaining bianry image is:
Predetermined threshold value, is divided into prospect, background two images, prospect n by original image
1, csum, m
1represent that the prospect under present threshold value is counted, moment of mass, average gray; Background n
2, sum-csum, m
2represent that the background under present threshold value is counted, moment of mass, average gray;
When getting optimal threshold, background and prospect difference maximum, adopt maximum between-cluster variance to select optimal threshold as weighing the standard of difference,
The derivation of maximum variance between clusters: note t is the segmentation threshold of prospect and background, and prospect is counted and accounted for image scaled is w
0, average gray is u
0; Background is counted and accounted for image scaled is w
1, average gray is u
1, then the overall average gray scale of image is: u=w
0* u
0+ w
1* u
1,
The variance of prospect and background map picture: g=w
0* (u
0-u) * (u
0-u)+w
1* (u
1-u) * (u
1-u)=w
0* w
1* (u
0-u
1) * (u
0-u
1),
When variance g is maximum, can think that now prospect and background difference are maximum, namely gray scale is now optimal threshold.
By display screen module defect inspection method, can filter the bubble on product protection film, cut, the unsubstantiality defect such as dirty, prevent the generation of false-alarm phenomenon, thus improve the accuracy to display screen module defects detection.
Accompanying drawing explanation
Fig. 1 is the structural representation to the detection system that display screen module detects;
Fig. 2 is the flow chart of steps of display screen module defect inspection method of the present invention.
Embodiment
Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited thereto.
As shown in Figure 1, it is the structural representation to the detection system 20 that display screen module 10 detects.Detection system 20 comprises the camera 22 of taking pictures to display screen module 10, and to the polishing light source 24 that display screen module 10 irradiates.Corresponding with said detecting system 20, a kind of detection method of display screen module defect is provided.
As shown in Figure 2, it is the flow chart of steps of display screen module defect inspection method of the present invention.
Display screen module defect inspection method, comprises the steps:
Step S1, detects and records the unsubstantiality defect on display screen module surface protection film;
Step S2, detects and records display screen module under illuminating state, all defect that can find;
Step S3, in all defect that step S2 finds, the unsubstantiality defect recorded in filtering step S1, namely remainder is the real defect of display screen module.
Wherein in step sl,
Step S1 comprises:
Step S11, when display screen module is under not illuminating state or when being switched to black picture, lights the polishing light source of display screen module outside, and the image under obtaining current state;
Step S12, carries out binaryzation to the image in step S11, obtains bianry image, and wherein high light tone area part is the unsubstantiality defect of display screen module surface protection film.
Wherein in step s 2,
Step S2 comprises:
Step S21, when display screen module is switched to light tone picture, closes the polishing light source of display screen module outside, and the image under obtaining current state;
Step S22, carries out binaryzation to the image in step S21, obtains bianry image.
Wherein in step s3,
Step S3 comprises:
Step S31, deducts the bianry image that step S12 obtains by the bianry image obtained in step S22, obtain a new bianry image, the real defect being display screen module of its high light tone region display;
Step3Image(i,j)＝Step2Image(i,j)–Step1Image(i,j)，
Step3Image (I, j) in formula, Step2Image (i, j), Step1Image (i, j) represent step S31 respectively, step S22, and the brightness value of the pixel of the i-th row jth row in the bianry image that obtains of step S12;
Step S32, the bianry image that step S31 obtains searches connected region and is real defect.
Wherein, the method obtaining bianry image is:
Predetermined threshold value, is divided into prospect, background two images, prospect n by original image
1, csum, m
1represent that the prospect under present threshold value is counted, moment of mass, average gray; Background n
2, sum-csum, m
2represent that the background under present threshold value is counted, moment of mass, average gray;
When getting optimal threshold, background and prospect difference maximum, adopt maximum between-cluster variance to select optimal threshold as weighing the standard of difference,
The derivation of maximum variance between clusters: note t is the segmentation threshold of prospect and background, and prospect is counted and accounted for image scaled is w
0, average gray is u
0; Background is counted and accounted for image scaled is w
1, average gray is u
1, then the overall average gray scale of image is: u=w
0* u
0+ w
1* u
1,
The variance of prospect and background map picture: g=w
0* (u
0-u) * (u
0-u)+w
1* (u
1-u) * (u
1-u)=w
0* w
1* (u
0-u
1) * (u
0-u
1),
When variance g is maximum, can think that now prospect and background difference are maximum, namely gray scale is now optimal threshold.
By display screen module defect inspection method, can filter the bubble on product protection film, cut, the unsubstantiality defect such as dirty, prevent the generation of false-alarm phenomenon, thus improve the accuracy to display screen module defects detection.
Above-described embodiment is the present invention's preferably embodiment; but embodiments of the present invention are not restricted to the described embodiments; change, the modification done under other any does not deviate from Spirit Essence of the present invention and principle, substitute, combine, simplify; all should be the substitute mode of equivalence, be included within protection scope of the present invention.
Claims (5)
1. a display screen module defect inspection method, is characterized in that, comprises the steps:
Step S1, detects and records the unsubstantiality defect on display screen module surface protection film;
Step S2, detects and records display screen module under illuminating state, all defect that can find;
Step S3, in all defect that step S2 finds, the unsubstantiality defect recorded in filtering step S1, namely remainder is the real defect of display screen module.
2. display screen module defect inspection method according to claim 1, it is characterized in that, step S1 comprises:
Step S11, when display screen module is under not illuminating state or when being switched to black picture, lights the polishing light source of display screen module outside, and the image under obtaining current state;
Step S12, carries out binaryzation to the image in step S11, obtains bianry image, and wherein high light tone area part is the unsubstantiality defect of display screen module surface protection film.
3. display screen module defect inspection method according to claim 2, it is characterized in that, step S2 comprises:
Step S21, when display screen module is switched to light tone picture, closes the polishing light source of display screen module outside, and the image under obtaining current state;
Step S22, carries out binaryzation to the image in step S21, obtains bianry image.
4. display screen module defect inspection method according to claim 3, it is characterized in that, step S3 comprises:
Step S31, deducts the bianry image that step S12 obtains by the bianry image obtained in step S22, obtain a new bianry image, the real defect being display screen module of its high light tone region display;
Step3Image(i,j)＝Step2Image(i,j)–Step1Image(i,j)，
Step3Image (i, j) in formula, Step2Image (i, j), Step1Image (i, j) represent step S31 respectively, step S22, and the brightness value of the pixel of the i-th row jth row in the bianry image that obtains of step S12;
Step S32, the bianry image that step S31 obtains searches connected region and is real defect.
5. display screen module defect inspection method according to claim 4, is characterized in that, the method obtaining bianry image is:
Predetermined threshold value, is divided into prospect, background two images, prospect n by original image
1, csum, m
1represent that the prospect under present threshold value is counted, moment of mass, average gray; Background n
2, sum-csum, m
2represent that the background under present threshold value is counted, moment of mass, average gray;
When getting optimal threshold, background and prospect difference maximum, adopt maximum between-cluster variance to select optimal threshold as weighing the standard of difference,
The derivation of maximum variance between clusters: note t is the segmentation threshold of prospect and background, and prospect is counted and accounted for image scaled is w
0, average gray is u
0; Background is counted and accounted for image scaled is w
1, average gray is u
1, then the overall average gray scale of image is: u=w
0* u
0+ w
1* u
1,
The variance of prospect and background map picture: g=w
0* (u
0-u) * (u
0-u)+w
1* (u
1-u) * (u
1-u)=w
0* w
1* (u
0-u
1) * (u
0-u
1),
When variance g is maximum, can think that now prospect and background difference are maximum, namely gray scale is now optimal threshold.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510859990.9A CN105424723A (en) | 2015-11-28 | 2015-11-28 | Detecting method for defects of display screen module |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510859990.9A CN105424723A (en) | 2015-11-28 | 2015-11-28 | Detecting method for defects of display screen module |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105424723A true CN105424723A (en) | 2016-03-23 |
Family
ID=55503072
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510859990.9A Pending CN105424723A (en) | 2015-11-28 | 2015-11-28 | Detecting method for defects of display screen module |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105424723A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106022379A (en) * | 2016-05-23 | 2016-10-12 | 佛山绿怡信息科技有限公司 | Method and device for detecting depreciation degree of screen |
| CN106872483A (en) * | 2017-02-04 | 2017-06-20 | 大连益盛达智能科技有限公司 | Optical detection apparatus are solved because of the method for the aeration detection in transparent material |
| CN107390393A (en) * | 2017-07-24 | 2017-11-24 | 惠州高视科技有限公司 | Layer method of appraising is answered after a kind of liquid crystal module defects detection |
| CN109656033A (en) * | 2017-10-12 | 2019-04-19 | 凌云光技术集团有限责任公司 | A kind of method and device for distinguishing liquid crystal display dust and defect |
| CN109752870A (en) * | 2019-01-31 | 2019-05-14 | 电子科技大学中山学院 | Electrophoresis electronic paper ghost detection system and detection method |
| CN109801322A (en) * | 2017-11-16 | 2019-05-24 | 合肥欣奕华智能机器有限公司 | A kind of light leak test method and device |
| CN110261408A (en) * | 2019-07-30 | 2019-09-20 | 云谷（固安）科技有限公司 | Display module defect detecting device and method |
| CN111179795A (en) * | 2020-02-19 | 2020-05-19 | 京东方科技集团股份有限公司 | Display panel detection method and device |
| CN111374608A (en) * | 2018-12-29 | 2020-07-07 | 尚科宁家(中国)科技有限公司 | Dirt detection method, device, equipment and medium for lens of sweeping robot |
| CN112419228A (en) * | 2020-10-14 | 2021-02-26 | 惠州高视科技有限公司 | Method and device for detecting three-dimensional edge defect of cover plate |
| CN113390611A (en) * | 2021-05-27 | 2021-09-14 | 北京兆维科技开发有限公司 | Screen defect detection method |
| CN116754566A (en) * | 2023-08-17 | 2023-09-15 | 绍兴旭源新材料科技有限公司 | Flexible folding screen protective film detection method |
| CN117191809A (en) * | 2023-08-30 | 2023-12-08 | 宿州绍宸智能科技有限公司 | Glass detection equipment fault monitoring and early warning system based on data analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1648645A (en) * | 2004-01-30 | 2005-08-03 | 三星电子株式会社 | Apparatus and method to inspect display panels |
| CN101419176A (en) * | 2007-10-26 | 2009-04-29 | 比亚迪股份有限公司 | Surface flaw detecting method and device |
| CN102930265A (en) * | 2012-09-19 | 2013-02-13 | 广州市中崎商业机器有限公司 | Method and device for scanning multiple identity cards |
| CN103217436A (en) * | 2013-03-06 | 2013-07-24 | 京东方科技集团股份有限公司 | Backlight module group defect detection method and equipment |
| CN104298753A (en) * | 2014-10-17 | 2015-01-21 | 重庆市云日信息技术有限公司 | Personnel assessment method based on face image processing |
-
2015
- 2015-11-28 CN CN201510859990.9A patent/CN105424723A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1648645A (en) * | 2004-01-30 | 2005-08-03 | 三星电子株式会社 | Apparatus and method to inspect display panels |
| CN101419176A (en) * | 2007-10-26 | 2009-04-29 | 比亚迪股份有限公司 | Surface flaw detecting method and device |
| CN102930265A (en) * | 2012-09-19 | 2013-02-13 | 广州市中崎商业机器有限公司 | Method and device for scanning multiple identity cards |
| CN103217436A (en) * | 2013-03-06 | 2013-07-24 | 京东方科技集团股份有限公司 | Backlight module group defect detection method and equipment |
| CN104298753A (en) * | 2014-10-17 | 2015-01-21 | 重庆市云日信息技术有限公司 | Personnel assessment method based on face image processing |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106022379A (en) * | 2016-05-23 | 2016-10-12 | 佛山绿怡信息科技有限公司 | Method and device for detecting depreciation degree of screen |
| CN106872483A (en) * | 2017-02-04 | 2017-06-20 | 大连益盛达智能科技有限公司 | Optical detection apparatus are solved because of the method for the aeration detection in transparent material |
| CN107390393A (en) * | 2017-07-24 | 2017-11-24 | 惠州高视科技有限公司 | Layer method of appraising is answered after a kind of liquid crystal module defects detection |
| CN109656033A (en) * | 2017-10-12 | 2019-04-19 | 凌云光技术集团有限责任公司 | A kind of method and device for distinguishing liquid crystal display dust and defect |
| CN109656033B (en) * | 2017-10-12 | 2021-07-02 | 凌云光技术股份有限公司 | Method and device for distinguishing dust and defects of liquid crystal display screen |
| CN109801322A (en) * | 2017-11-16 | 2019-05-24 | 合肥欣奕华智能机器有限公司 | A kind of light leak test method and device |
| CN111374608A (en) * | 2018-12-29 | 2020-07-07 | 尚科宁家(中国)科技有限公司 | Dirt detection method, device, equipment and medium for lens of sweeping robot |
| CN111374608B (en) * | 2018-12-29 | 2021-08-03 | 尚科宁家(中国)科技有限公司 | Dirt detection method, device, equipment and medium for lens of sweeping robot |
| CN109752870A (en) * | 2019-01-31 | 2019-05-14 | 电子科技大学中山学院 | Electrophoresis electronic paper ghost detection system and detection method |
| CN110261408A (en) * | 2019-07-30 | 2019-09-20 | 云谷（固安）科技有限公司 | Display module defect detecting device and method |
| CN110261408B (en) * | 2019-07-30 | 2021-11-23 | 云谷（固安）科技有限公司 | Display module defect detection device and method |
| CN111179795B (en) * | 2020-02-19 | 2023-07-21 | 京东方科技集团股份有限公司 | Display panel detection method and device |
| CN111179795A (en) * | 2020-02-19 | 2020-05-19 | 京东方科技集团股份有限公司 | Display panel detection method and device |
| CN112419228A (en) * | 2020-10-14 | 2021-02-26 | 惠州高视科技有限公司 | Method and device for detecting three-dimensional edge defect of cover plate |
| CN112419228B (en) * | 2020-10-14 | 2022-04-05 | 高视科技(苏州)有限公司 | Method and device for detecting three-dimensional edge defect of cover plate |
| CN113390611A (en) * | 2021-05-27 | 2021-09-14 | 北京兆维科技开发有限公司 | Screen defect detection method |
| CN113390611B (en) * | 2021-05-27 | 2022-07-12 | 北京兆维科技开发有限公司 | Screen defect detection method |
| CN116754566A (en) * | 2023-08-17 | 2023-09-15 | 绍兴旭源新材料科技有限公司 | Flexible folding screen protective film detection method |
| CN116754566B (en) * | 2023-08-17 | 2023-10-31 | 绍兴旭源新材料科技有限公司 | Flexible folding screen protective film detection method |
| CN117191809A (en) * | 2023-08-30 | 2023-12-08 | 宿州绍宸智能科技有限公司 | Glass detection equipment fault monitoring and early warning system based on data analysis |
| CN117191809B (en) * | 2023-08-30 | 2024-03-22 | 宿州绍宸智能科技有限公司 | Glass detection equipment fault monitoring and early warning system based on data analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105424723A (en) | Detecting method for defects of display screen module | |
| US8854431B2 (en) | Optical self-diagnosis of a stereoscopic camera system | |
| CN104749184B (en) | Automatic optical detection method and system | |
| CN107749268B (en) | Screen detection method and equipment | |
| CN103792705B (en) | Detecting method and detecting device for detecting substrate defects | |
| EP2641230A1 (en) | Method and apparatus for detection and removal of rain from videos using temporal and spatiotemporal properties | |
| JP2017084364A (en) | Parking space detection device, electronic instrument, and method | |
| CN107644538B (en) | Traffic signal lamp identification method and device | |
| CN107390393B (en) | Re-judgment layering method after defect detection of liquid crystal module | |
| CN110021012B (en) | Mobile phone lens window glass defect detection method based on machine vision technology | |
| CN103186887A (en) | Image demisting device and image demisting method | |
| CN104568989A (en) | Glass substrate defect detecting method | |
| WO2017120796A1 (en) | Pavement distress detection method and apparatus, and electronic device | |
| CN108107611B (en) | Self-adaptive defect detection method and device and electronic equipment | |
| CN104809725A (en) | Cloth defect visual identify detecting device and method | |
| CN106228517A (en) | Image collecting device image-forming component defect calibration steps | |
| CN104749801B (en) | High Precision Automatic optical detecting method and system | |
| CN110007493A (en) | Broken bright spot detection method in liquid crystal display | |
| CN103245666B (en) | Automatic detecting method for appearance defects of storage battery polar plate | |
| CN111426693A (en) | Quality defect detection system and detection method thereof | |
| CN107169969B (en) | Highway dangerous rock collapse deposit size measurement and alarm system based on FPGA | |
| CN104349161B (en) | The dirty decision method of camera module image | |
| JP2009229197A (en) | Linear defect detecting method and device | |
| US20160110606A1 (en) | Image recognizing apparatus and image recognizing method | |
| US9270984B2 (en) | Camera with dust checking function |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160323 |
|
| RJ01 | Rejection of invention patent application after publication |