CN106851264A - Camera module group lens surface inspecting method and device - Google Patents
Camera module group lens surface inspecting method and device Download PDFInfo
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- CN106851264A CN106851264A CN201710226099.0A CN201710226099A CN106851264A CN 106851264 A CN106851264 A CN 106851264A CN 201710226099 A CN201710226099 A CN 201710226099A CN 106851264 A CN106851264 A CN 106851264A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
- H04N17/002—Diagnosis, testing or measuring for television systems or their details for television cameras
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Abstract
A kind of camera module group lens surface inspecting method is the embodiment of the invention provides, is comprised the following steps：Obtain the imaging picture of camera module；It is calculated as the ensemble average brightness value as picture；Imaging picture is divided into several groups；Each group is scanned using default frame, and analyzes whether the regional luminance being made up of some continuous image vegetarian refreshments in group is less than ensemble average brightness value；Judge that whether dirty camera lens surface is according to scanning analysis result.On the other hand, the embodiment of the present invention additionally provides a kind of camera module group lens surface detection apparatus.The embodiment of the present invention, can automatic decision camera module group lens surface it is whether dirty, detection efficiency is high, can accurately reflect soiled condition, and recognizes that cost of labor is low without manually naked eyes.
Description
Technical field：
The present invention relates to the electronic product surface inspecting method and device of a kind of optical arena, especially a kind of camera module mirror
Head surface detection method and device.
Background technology：
In optical field, for the camera lens surface of camera module, there is strict clean free of contamination quality requirement, and
The camera lens Surface testing of existing camera module, typically manually naked eyes are recognized, with stronger subjectivity, easily by various factors
Influence, it is impossible to objectively respond soiled condition, cost of labor is higher, and efficiency is low.
The content of the invention：
To overcome the camera lens surface of existing camera module, manually naked eyes are recognized, cost of labor is higher, and efficiency is low asks
Topic, the embodiment of the invention provides a kind of camera module group lens surface inspecting method.
Camera module group lens surface inspecting method, comprises the following steps：
Obtain the imaging picture of camera module；
It is calculated as the ensemble average brightness value as picture；
Imaging picture is divided into several groups；
Each group is scanned using default frame, and it is bright to analyze the region being made up of some continuous image vegetarian refreshments in group
Whether degree is less than ensemble average brightness value；
Judge that whether dirty camera lens surface is according to scanning analysis result.
On the other hand, the embodiment of the present invention additionally provides a kind of camera module group lens surface detection apparatus.
Camera module group lens surface detection apparatus, including：
Acquisition module, the imaging picture for obtaining camera module；
Brightness calculation module, for being calculated as the ensemble average brightness value as picture；
Group division module, for imaging picture to be divided into several groups；
Scanning analysis module, for being scanned to each group using default frame, and is analyzed in group by some continuous
Whether the regional luminance of pixel composition is less than ensemble average brightness value；
Judge module, for judging that whether dirty camera lens surface is according to scanning analysis result.
The embodiment of the present invention, can automatic decision camera module group lens surface it is whether dirty, detection efficiency is high, can accurately reflect
Soiled condition, and recognize that cost of labor is low without manually naked eyes.
Brief description of the drawings：
Technical scheme in order to illustrate more clearly the embodiments of the present invention, below will be to that will make needed for embodiment description
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for
For those of ordinary skill in the art, on the premise of not paying creative work, other can also be obtained according to these accompanying drawings
Accompanying drawing.
Fig. 1 is the schematic flow sheet of the first embodiment of camera module group lens surface inspecting method of the invention；
Fig. 2 is the schematic flow sheet of the second embodiment of camera module group lens surface inspecting method of the invention；
Fig. 3 is the structural representation of the first embodiment of camera module group lens surface detection apparatus of the invention；
Fig. 4 is the structural representation of the second embodiment of camera module group lens surface detection apparatus of the invention.
Specific embodiment：
In order that technical problem solved by the invention, technical scheme and beneficial effect become more apparent, below in conjunction with
Drawings and Examples, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only used
To explain the present invention, it is not intended to limit the present invention.
Fig. 1 is the schematic flow sheet of the first embodiment of camera module group lens surface inspecting method of the invention, and it includes：
Step S101, obtains the imaging picture of camera module.
In this step, camera module can be placed under white background and obtain its imaging picture.
In this step, the imaging picture of acquisition can be the image of .bmp forms.
Step S102, is calculated as the ensemble average brightness value as picture.
In this step, when ensemble average brightness value is calculated, it is kept into as the R values in picture three primary colours are constant, mainly takes
The G values of picture are imaged to calculate.
Step S103, several groups are divided into by imaging picture.
Step S104, is scanned using default frame to each group, and is analyzed in group by some continuous image vegetarian refreshments groups
Into regional luminance whether be less than ensemble average brightness value.
In this step, the size for presetting frame can be 16*16~200*200 pixel size.
Step S105, judges that whether dirty camera lens surface is according to scanning analysis result.
In this step, if the regional luminance being made up of some continuous image vegetarian refreshments in group is less than ensemble average brightness value,
Then judge that camera lens surface is present dirty.
The embodiment of the present invention, can automatic decision camera module group lens surface it is whether dirty, detection efficiency is high, can accurately reflect
Soiled condition, and recognize that cost of labor is low without manually naked eyes.
Fig. 2 is the schematic flow sheet of the second embodiment of camera module group lens surface inspecting method of the invention, and it includes：
Step S201, obtains the imaging picture of camera module.
In this step, camera module can be placed under white background and obtain its imaging picture.
In this step, the imaging picture of acquisition can be the image of .bmp forms.
Step S202, is calculated as the ensemble average brightness value as picture.
In this step, when ensemble average brightness value is calculated, it is kept into as the R values in picture three primary colours are constant, mainly takes
The G values of picture are imaged to calculate.
Step S203, divides m elementary cell on the imaging picture of camera module, and each elementary cell is with n pixel
Point is an elementary cell.
Specifically, in this step, 1000 elementary cells are divided on the imaging picture of camera module.
More specifically, in this step, each elementary cell is an elementary cell with 16 pixels.
Step S204, calculates the cell-average brightness value of each elementary cell respectively.
In this step, the cell-average brightness value of each elementary cell may be different.
Neighboring unit cells are merged into several groups by step S205 according to cell-average brightness value.
In this step, if the cell-average brightness value of neighboring unit cells is identical or cell-average brightness value is being allowed
In the range of, then neighboring unit cells are merged into a group.
Step S206, obtains the brightness value of any pixel point of default inframe.
In this step, group is scanned using default frame, and obtains the brightness value of any pixel point of default inframe.
In this step, the size for presetting frame can be 16*16~200*200 pixel size.
Step S207, pixel brightness value is compared with ensemble average brightness value.
Whether step S208, judge pixel brightness value less than ensemble average brightness value
In this step, if it is judged that being yes, then flow enters step S209.
Step S209, bad point is identified as by the pixel.
Step S210, the bad point number of the default inframe of statistics.
Whether step S211, judge the ratio between bad point number and default inframe all pixels point number more than setting value
Specifically, in this step, mainly by whether judging the ratio between bad point number and default inframe all pixels point number
More than threshold value 0.75, so as to obtain bad point proportion.
In this step, if it is judged that being yes, then flow enters step S212.
Step S212, is stain region by the zone marker of default inframe.
Step S213, expands default frame.
In this step, default frame is gradually expanded by 1 pixel grade.It is 16*16 pixel such as to preset frame initial size
Point size, can be 16*17 pixel size after expansion, until same with scanned group big.
Step S214, judges whether default frame area is less than group
It is main by judging whether default frame area is less than group in this step, so as to learn default frame whether gradually
The process of expansion completes the scanning analysis of whole group to the group being scanned.
In this step, if it is judged that being yes, then flow return to step S206；Otherwise, flow enters step S215.
Step S215, judges whether to complete scanning analysis to all groups
In this step, if it is judged that being yes, then flow terminates；Otherwise, flow return to step S206.
The embodiment of the present invention, by the scanning analysis to group, can automatic decision camera module group lens surface it is whether dirty,
Detection efficiency is high, can accurately reflect soiled condition, and recognize that cost of labor is low without manually naked eyes.
The embodiment to camera module group lens surface inspecting method of the invention is discussed in detail above.Below will be corresponding
It is further elaborated in the device of the above method.
Fig. 3 is the structural representation of the first embodiment of camera module group lens surface detection apparatus of the invention, the shooting
Module group lens surface detection apparatus 100 include：Acquisition module 110, brightness calculation module 120, group division module 130, scanning
Analysis module 140 and judge module 150.
Acquisition module 110, the imaging picture for obtaining camera module；
Brightness calculation module 120, for being calculated as the ensemble average brightness value as picture；
Group division module 130, for imaging picture to be divided into several groups；
Scanning analysis module 140, for being scanned to each group using default frame, and if analyzing in group by involvement
Whether the regional luminance of continuous pixel composition is less than ensemble average brightness value；
Judge module 150, for judging that whether dirty camera lens surface is according to scanning analysis result.
The embodiment of the present invention, by this camera module group lens surface detection apparatus, can automatic decision camera module group lens table
Whether dirty face is, and detection efficiency is high, can accurately reflect soiled condition, and recognize that cost of labor is low without manually naked eyes.
Fig. 4 is the structural representation of the second embodiment of camera module group lens surface detection apparatus of the invention, the shooting
Module group lens surface detection apparatus 200 include：Acquisition module 210, brightness calculation module 220, group division module 230, scanning
Analysis module 240 and judge module 250.
Acquisition module 210, the imaging picture for obtaining camera module.
Brightness calculation module 220, for being calculated as the ensemble average brightness value as picture.
Group division module 230, for imaging picture to be divided into several groups.
Scanning analysis module 240, for being scanned to each group using default frame, and if analyzing in group by involvement
Whether the regional luminance of continuous pixel composition is less than ensemble average brightness value.
Judge module 250, for judging that whether dirty camera lens surface is according to scanning analysis result.
Further, group division module 230 includes：
Division unit 231, for dividing m elementary cell on the imaging picture of camera module, each elementary cell is with n
Individual pixel is an elementary cell.
Computing unit 232, the cell-average brightness value for calculating each elementary cell respectively.
Combining unit 233, for neighboring unit cells to be merged into several groups according to cell-average brightness value.
Yet further, scanning analysis module 240 includes preliminary scan analytic unit 241 and expands scanning analysis unit
242。
Preliminary scan analytic unit 241 includes：
Obtain subelement, the brightness value of any pixel point for obtaining default inframe；
Comparing subunit, for pixel brightness value to be compared with ensemble average brightness value；
Bad point recognizes subelement, for when pixel brightness value is less than ensemble average brightness value, by pixel identification
It is bad point；
Statistics subelement, for counting bad point number；
Mark subelement, for when the ratio between bad point number and default inframe all pixels point number are more than setting value, will
The zone marker of default inframe is stain region.
Expand scanning analysis unit 242, be mainly used in expanding default frame.
Camera module group lens are imaged picture and are scanned analysis by the embodiment of the present invention by this detection means, can be automatic
Judge that whether dirty camera module group lens surface is, detection efficiency is high, can accurately reflect soiled condition, and know without manually naked eyes
Not, cost of labor is low.
It is as described above to combine one or more implementation method that particular content is provided, does not assert specific reality of the invention
Apply and be confined to these explanations.It is all approximate with the method for the present invention, structure etc., identical, or for present inventive concept under the premise of
Some technology deduction or replace are made, should all be considered as protection scope of the present invention.
Claims (8)
1. camera module group lens surface inspecting method, it is characterised in that comprise the following steps：
Obtain the imaging picture of camera module；
It is calculated as the ensemble average brightness value as picture；
Imaging picture is divided into several groups；
Each group is scanned using default frame, and analyzes the regional luminance being made up of some continuous image vegetarian refreshments in group is
It is no less than ensemble average brightness value；
Judge that whether dirty camera lens surface is according to scanning analysis result.
2. camera module group lens surface inspecting method according to claim 1, it is characterised in that " be divided into imaging picture
The step of several groups ", includes：
M elementary cell is divided on the imaging picture of camera module, each elementary cell is substantially single with n pixel as one
Unit；
The cell-average brightness value of each elementary cell is calculated respectively；
Neighboring unit cells are merged into by several groups according to cell-average brightness value.
3. camera module group lens surface inspecting method according to claim 1 and 2, it is characterised in that " using default frame pair
Each group is scanned, and whether analyze the regional luminance being made up of some continuous image vegetarian refreshments in group bright less than ensemble average
The step of angle value ", includes：Preliminary scan analytical procedure, the preliminary scan analytical procedure includes：
Obtain the brightness value of any pixel point of default inframe；
Pixel brightness value is compared with ensemble average brightness value；
If pixel brightness value is less than ensemble average brightness value, the pixel is identified as bad point；
Statistics bad point number；
If the ratio between bad point number and default inframe all pixels point number are more than setting value, by the zone marker of default inframe
It is stain region.
4. camera module group lens surface inspecting method according to claim 3, it is characterised in that " using default frame to every
One group is scanned, and analyzes whether the regional luminance being made up of some continuous image vegetarian refreshments in group is less than ensemble average brightness
The step of value ", also includes：Expand scanning analysis step, the expansion scanning analysis step includes：
Expand default frame；
If default frame area is less than group, repeatedly preliminary scan analytical procedure.
5. camera module group lens surface detection apparatus, it is characterised in that including：
Acquisition module, the imaging picture for obtaining camera module；
Brightness calculation module, for being calculated as the ensemble average brightness value as picture；
Group division module, for imaging picture to be divided into several groups；
Scanning analysis module, for being scanned to each group using default frame, and is analyzed in group by some contiguous pixels
Whether the regional luminance of point composition is less than ensemble average brightness value；
Judge module, for judging that whether dirty camera lens surface is according to scanning analysis result.
6. camera module group lens surface detection apparatus according to claim 5, it is characterised in that group division module bag
Include：
Division unit, for dividing m elementary cell on the imaging picture of camera module, each elementary cell is with n pixel
Point is an elementary cell；
Computing unit, the cell-average brightness value for calculating each elementary cell respectively；
Combining unit, for neighboring unit cells to be merged into several groups according to cell-average brightness value.
7. camera module group lens surface detection apparatus according to claim 5 or 6, it is characterised in that scanning analysis module
Including preliminary scan analytic unit, the preliminary scan analytic unit includes：
Obtain subelement, the brightness value of any pixel point for obtaining default inframe；
Comparing subunit, for pixel brightness value to be compared with ensemble average brightness value；
Bad point recognizes subelement, for when pixel brightness value is less than ensemble average brightness value, the pixel being identified as bad
Point；
Statistics subelement, for counting bad point number；
Mark subelement, for when the ratio between bad point number and default inframe all pixels point number are more than setting value, will be default
The zone marker of inframe is stain region.
8. camera module group lens surface detection apparatus according to claim 7, it is characterised in that scanning analysis module is also wrapped
Expansion scanning analysis unit is included, the expansion scanning analysis unit is used to expand default frame.
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| CN201710226099.0A CN106851264B (en) | 2017-04-08 | 2017-04-08 | Camera module lens surface detection method and device |
| CN202010112800.8A CN111277822B (en) | 2017-04-08 | 2017-04-08 | Camera module lens surface detection device |
| CN202010112307.6A CN111294589B (en) | 2017-04-08 | 2017-04-08 | Camera module lens surface detection method |
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| CN202010112800.8A Division CN111277822B (en) | 2017-04-08 | 2017-04-08 | Camera module lens surface detection device |
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109800654A (en) * | 2018-12-24 | 2019-05-24 | 百度在线网络技术（北京）有限公司 | Vehicle-mounted camera detection processing method, apparatus and vehicle |
| CN110446025A (en) * | 2019-06-25 | 2019-11-12 | 盐城华昱光电技术有限公司 | Camera module detection system and method applied to electronic equipment |
| CN111083467A (en) * | 2019-12-09 | 2020-04-28 | 横店集团东磁有限公司 | Dirty mode of snatching of module of making a video recording |
| CN111246204A (en) * | 2020-03-24 | 2020-06-05 | 昆山丘钛微电子科技有限公司 | Relative brightness deviation-based dirt detection method and device |
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Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111726612A (en) * | 2020-07-07 | 2020-09-29 | 歌尔科技有限公司 | Lens module dirt detection method, system, equipment and computer storage medium |
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| CN113570582B (en) * | 2021-07-30 | 2022-07-29 | 上海集成电路制造创新中心有限公司 | Camera cover plate cleanliness detection method and detection device |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070012869A1 (en) * | 2005-05-13 | 2007-01-18 | Mullin Christopher S | Low power glare sensor |
| CN101329281A (en) * | 2007-06-20 | 2008-12-24 | 佛山普立华科技有限公司 | System and method for testing image sensing wafer stain and |
| CN103179428A (en) * | 2011-12-23 | 2013-06-26 | 鸿富锦精密工业（深圳）有限公司 | System and method for testing camera module stains |
| CN104135660A (en) * | 2014-08-14 | 2014-11-05 | 广东光阵光电科技有限公司 | Detection method of contamination of image pickup module and detection system |
| CN104185019A (en) * | 2014-07-24 | 2014-12-03 | 青岛歌尔声学科技有限公司 | Camera stain detecting method and device |
| CN104867159A (en) * | 2015-06-05 | 2015-08-26 | 北京大恒图像视觉有限公司 | Stain detection and classification method and device for sensor of digital camera |
| CN105163114A (en) * | 2015-08-21 | 2015-12-16 | 深圳创维－Rgb电子有限公司 | Method and system for detecting screen dead pixel based on camera |
| CN106412573A (en) * | 2016-10-26 | 2017-02-15 | 歌尔科技有限公司 | Method and device for detecting lens stain |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7103208B2 (en) * | 2002-08-26 | 2006-09-05 | Eastman Kodak Company | Detecting and classifying blemishes on the transmissive surface of an image sensor package |
| JP4130435B2 (en) * | 2004-11-30 | 2008-08-06 | 本田技研工業株式会社 | Abnormality detection device for imaging device |
| CN102378037A (en) * | 2010-08-04 | 2012-03-14 | 致伸科技股份有限公司 | Image test method of image acquisition device and image test device using same |
| JP5994301B2 (en) * | 2011-06-20 | 2016-09-21 | 株式会社リコー | Image processing apparatus, information processing apparatus, method, program, and recording medium |
| CN104093016B (en) * | 2014-06-12 | 2016-04-13 | 华南理工大学 | A kind of dirty detection method of camera module and system |
| CN204964854U (en) * | 2015-08-27 | 2016-01-13 | 丁雪勤 | High pixel module of making a video recording |
| CN106231297B (en) * | 2016-08-29 | 2019-03-19 | 深圳天珑无线科技有限公司 | The detection method and device of camera |
-
2017
- 2017-04-08 CN CN201710226099.0A patent/CN106851264B/en active Active
- 2017-04-08 CN CN202010112307.6A patent/CN111294589B/en active Active
- 2017-04-08 CN CN202010112800.8A patent/CN111277822B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070012869A1 (en) * | 2005-05-13 | 2007-01-18 | Mullin Christopher S | Low power glare sensor |
| CN101329281A (en) * | 2007-06-20 | 2008-12-24 | 佛山普立华科技有限公司 | System and method for testing image sensing wafer stain and |
| CN103179428A (en) * | 2011-12-23 | 2013-06-26 | 鸿富锦精密工业（深圳）有限公司 | System and method for testing camera module stains |
| CN104185019A (en) * | 2014-07-24 | 2014-12-03 | 青岛歌尔声学科技有限公司 | Camera stain detecting method and device |
| CN104135660A (en) * | 2014-08-14 | 2014-11-05 | 广东光阵光电科技有限公司 | Detection method of contamination of image pickup module and detection system |
| CN104867159A (en) * | 2015-06-05 | 2015-08-26 | 北京大恒图像视觉有限公司 | Stain detection and classification method and device for sensor of digital camera |
| CN105163114A (en) * | 2015-08-21 | 2015-12-16 | 深圳创维－Rgb电子有限公司 | Method and system for detecting screen dead pixel based on camera |
| CN106412573A (en) * | 2016-10-26 | 2017-02-15 | 歌尔科技有限公司 | Method and device for detecting lens stain |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109800654A (en) * | 2018-12-24 | 2019-05-24 | 百度在线网络技术（北京）有限公司 | Vehicle-mounted camera detection processing method, apparatus and vehicle |
| CN112044862A (en) * | 2019-06-06 | 2020-12-08 | 中信戴卡股份有限公司 | Lens protection device and lens protection method |
| CN110446025A (en) * | 2019-06-25 | 2019-11-12 | 盐城华昱光电技术有限公司 | Camera module detection system and method applied to electronic equipment |
| CN111083467A (en) * | 2019-12-09 | 2020-04-28 | 横店集团东磁有限公司 | Dirty mode of snatching of module of making a video recording |
| CN113014908A (en) * | 2019-12-19 | 2021-06-22 | 西安诺瓦星云科技股份有限公司 | Image detection method, device and system and computer readable storage medium |
| CN111246204B (en) * | 2020-03-24 | 2022-02-01 | 昆山丘钛微电子科技有限公司 | Relative brightness deviation-based dirt detection method and device |
| CN111246204A (en) * | 2020-03-24 | 2020-06-05 | 昆山丘钛微电子科技有限公司 | Relative brightness deviation-based dirt detection method and device |
| CN111739012A (en) * | 2020-06-30 | 2020-10-02 | 重庆盛泰光电有限公司 | Camera module white spot detecting system based on turntable |
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| CN114040188A (en) * | 2021-09-26 | 2022-02-11 | 湖北三赢兴光电科技股份有限公司 | Camera module automatic testing method and system based on voice recognition |
| CN114023227A (en) * | 2021-11-04 | 2022-02-08 | 深圳利亚德光电有限公司 | Aging method of LED display device |
| CN115063342A (en) * | 2022-04-27 | 2022-09-16 | 珠海视熙科技有限公司 | Lens dead pixel detection method and device, electronic equipment and storage medium |
| CN115416615A (en) * | 2022-11-04 | 2022-12-02 | 江阴瑞兴塑料玻璃制品有限公司 | Monitoring and cleaning system for dust on front windshield of vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111294589A (en) | 2020-06-16 |
| CN111277822A (en) | 2020-06-12 |
| CN111277822B (en) | 2021-08-24 |
| CN111294589B (en) | 2021-08-24 |
| CN106851264B (en) | 2020-04-28 |
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