CN107013811B - A kind of pipeline liquid leakage monitoring method based on image procossing - Google Patents
A kind of pipeline liquid leakage monitoring method based on image procossing Download PDFInfo
- Publication number
- CN107013811B CN107013811B CN201710237423.9A CN201710237423A CN107013811B CN 107013811 B CN107013811 B CN 107013811B CN 201710237423 A CN201710237423 A CN 201710237423A CN 107013811 B CN107013811 B CN 107013811B
- Authority
- CN
- China
- Prior art keywords
- image
- detected
- pipeline
- gray level
- video
- 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.)
- Expired - Fee Related
Links
- 239000007788 liquid Substances 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000012544 monitoring process Methods 0.000 title claims abstract description 20
- 238000006243 chemical reaction Methods 0.000 claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 16
- 230000009466 transformation Effects 0.000 claims abstract description 5
- 238000001514 detection method Methods 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000005516 engineering process Methods 0.000 description 7
- 230000005856 abnormality Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000002547 anomalous effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
Abstract
The present invention relates to a kind of pipeline liquid leakage monitoring method based on image procossing.Its technical solution is：A color RGB image Y when frame pipeline does not leak is intercepted from video to be detected, is carried out greyscale transformation, is obtained gray level image y when pipeline does not leak.It is a video-frequency band to be detected per L frame RGB color images from the 1st frame RGB color image in video to be detected, takes a frame for image to be detected per n frames in video-frequency band to be detected.All image to be detected are switched into gray level image, obtain gray level image set Q1 to be detected, each of which width image is done into de-jitter and is averaged, then carries out binary conversion treatment.If the sum of all pixels value is less than the alert thresholds K of setting in the image Z after binary conversion treatment, pipeline liquid leakage does not occur for video-frequency band to be measured；It is on the contrary then pipeline liquid leakage occurs.The present invention has the characteristics that precision is high, complexity is low, processing procedure is short and can on-line real time monitoring.
Description
Technical field
The invention belongs to technical field of image processing.More particularly to a kind of pipeline liquid leakage monitoring based on image procossing
Method.
Background technology
In recent years, pipeline transportation is by feat of its stable and continuous, safety is good, freight volume is big, quality easily ensures, material damage
It loses less, take up an area less that the advantages such as low have become the preferred manners of the liquid transportings such as oil with freight charges.However, due to extraneous adverse environment
Transport pipeline aging caused by being grown with pipeline active time can be such that pipeline cracks, and then lead to the liquid transported in pipeline
It leaks, to cause great economic loss, while affecting the natural environment of surrounding.
As camera shoots the raising of precision, captured image and video can include more detailed information.
The high precision image of shooting is used in more complicated and fine environment, the monitoring leaked such as pipeline liquid.Staff is in face
When to a large amount of video and image data, it is difficult to there are enough energy and times to go whether observation pipeline leaks, so not
Pipe leakage may be found at the first time, and makes corresponding counter-measure, and subtle state change is difficult to discriminate between out
Come, when as small such as leakage flow.And the method with the naked eye observed can consume a large amount of human and material resources, cause resource unrestrained
Take.
A kind of " system and method for monitoring camera unit exception detection " (CN 101765025A) patented technology, the technology
It needs to carry out accuracy registration processing to image, not accounting for monitoring device during use has slight jitter conditions.So very
Difficulty prevents the case where inaccuracy occurred due to the smaller shake of video camera appearance.A kind of " video based on machine learning
Method for detecting abnormality " (CN 103763515A) patented technology, the technology have higher precision and standard to the identification of abnormality
True property has higher computation complexity it require that learning to anomalous video, increases the duration of processing video.
Invention content
The present invention is directed to overcome prior art defect, it is therefore an objective to provide a kind of precision is high, method is simple, processing procedure is short,
Adaptable and energy on line real-time monitoring the pipeline liquid leakage monitoring method based on image procossing.
In order to complete above-mentioned task, the technical solution adopted by the present invention comprises the concrete steps that：
The first step intercepts color RGB image Y when a frame pipeline does not leak from video to be detected；Using gray scale
Color RGB image Y when image algorithm does not leak the pipeline carries out greyscale transformation, when obtaining pipeline and not leaking
Gray level image y；The gray level image algorithm is：
Gray=R × 0.299+G × 0.587+B × 0.114 (1)
In formula (1)：R indicates the red component of colour RGB pictures；
G indicates the green component of colour RGB pictures；
B indicates the blue component of colour RGB pictures.
Second step, from the 1st frame RGB color image in the video to be detected, take every L frames RGB color image be one
A video-frequency band to be detected detects each video-frequency band to be detected one by one since first video-frequency band to be detected, each to be detected to regard
The detection method of frequency range is that third walks the~the five step.
It takes a frame RGB color image for image to be detected per n frame RGB color images in the video-frequency band to be detected, there is m
Frame image to be detected, then m × n=L, wherein：N is 2~100 natural numbers, the natural number that m is 10~1000.
Described m frames image to be detected is switched to gray level image by third step with formula (1), obtains gray level image set to be detected
Q1。
4th step, the gray level image y and gray level image set Q1 to be measured when the pipeline to that liquid leakage do not occur
In every piece image do de-jitter, obtain m width error images；Then the m width error image is averaged, is obtained
1 width average value image z.
5th step carries out binary conversion treatment to the average value image z, obtains the image Z after binary conversion treatment；Described two
Value processing algorithm be：
In formula (2)：Value indicates the pixel value of image Z after binary conversion treatment；
P indicates the original pixel value in average value image z；
TthresholdIndicate the binary-state threshold of setting, TthresholdFor 10~200 natural number.
If the sum of all pixels value is less than the alert thresholds K of setting, institute in the image Z after the 6th step, binary conversion treatment
It states video-frequency band to be measured and pipeline liquid leakage does not occur；If the sum of all pixels value is more than setting in the image Z after binary conversion treatment
Alert thresholds K, then the video-frequency band to be measured pipeline liquid leakage has occurred.
Natural numbers of the alert thresholds K set as 2550~100000.
The de-jitter is：A pixel P in gray level image is taken, the pixel P is in gray level image
Position coordinates are (a, b)；It is (a, b) that center is taken in gray level image y when pipeline does not leak, and size is the pixel of M × M
Block P1；The pixel p and each pixel in the block of pixels P1 are subtracted each other, the matrix P2 that size is M × M is obtained；Take square
The best match of pixel P in gray level image y when absolute value minimum value position does not leak as pipeline in battle array P2
The position of point；In gray level image y when the value of absolute value minimum in matrix P2 is not leaked as pixel P and pipeline
The difference of optimal match point.
Rest of pixels point in the gray level image is all done into above-mentioned processing, obtains error image.
Due to the adoption of the above technical scheme, the present invention has the following advantages that compared with prior art：
Pipeline liquid leakage monitoring method based on image procossing proposed by the invention only requires work people using simply
Member calibrates the state that pipeline liquid leakage does not occur, can quickly detect and pipeline liquid leakage whether occurs.
Present invention employs de-jitter technologies, high to the monitoring accuracy of pipeline liquid leakage, especially for outside
The fine jitter that video camera generates when boundary's environment is unstable has preferable robustness, adaptable.
The image when determination method that the present invention is used is not necessarily to that pipeline occurs liquid leakage carries out special training and
It practises, greatly reduces complexity, shorten the duration of processing.
The present invention is capable of the state of independent judgment single width testing image, meets the requirement of on-line real-time measuremen, can be in short-term
Interior discovery pipeline liquid leakage, and alarm is made, to increase the reliability of detection, it can greatly reduce cost of labor.
Therefore, the present invention has that precision is high, method is simple, processing procedure is short, adaptable and can realize online real-time supervise
The characteristics of survey.
Description of the drawings
Fig. 1 is gray level image y when liquid leakage does not occur for a frame pipeline of the invention；
Fig. 2 is the piece image in a kind of gray level image set Q1；
Fig. 3 is the image after the binary conversion treatment of image shown in Fig. 2；
Fig. 4 is gray level image y when another frame pipeline of the present invention does not leak；
Fig. 5 is the piece image in another gray level image set Q1；
Fig. 6 is the image after the binary conversion treatment of image shown in Fig. 5.
Specific implementation mode
The present invention will be further described with specific implementation mode below in conjunction with the accompanying drawings, not to the limit of its protection domain
System：
Embodiment 1
A kind of pipeline liquid leakage monitoring method based on image procossing.The specific steps of monitoring method described in the present embodiment
It is：
The first step intercepts color RGB image Y when a frame cabin pipeline does not leak from video to be detected；Using
Color RGB image Y when gray level image algorithm does not leak the pipeline carries out greyscale transformation, obtains as shown in Figure 1
Gray level image y when pipeline does not leak；The gray level image algorithm is：
Gray=R × 0.299+G × 0.587+B × 0.114 (1)
In formula (1)：R indicates the red component of colour RGB pictures；
G indicates the green component of colour RGB pictures；
B indicates the blue component of colour RGB pictures.
Second step, from the 1st frame RGB color image in the video to be detected, take every 100 frame RGB color image to be
One video-frequency band to be detected detects each video-frequency band to be detected one by one since first video-frequency band to be detected, each to be detected
The detection method of video-frequency band is that third walks the~the five step.
It is image to be detected that every 4 frame RGB color image, which takes a frame RGB color image, in the video-frequency band to be detected, is had
25 frame image to be detected.
Described 25 frame image to be detected is switched to gray level image by third step with formula (1), obtains gray level image set to be detected
Q1, Fig. 2 are the piece image in the gray level image set Q1 to be detected.
4th step, the gray level image y and gray level image set Q1 to be measured when the pipeline to that liquid leakage do not occur
In every piece image do de-jitter, obtain 25 width error images；Then the 25 width error image is averaged, is obtained
To 1 width average value image z.
5th step carries out binary conversion treatment to the average value image z, after obtaining binary conversion treatment as shown in Figure 3
Image Z；The algorithm of the binary conversion treatment is：
In formula (2)：Value indicates the pixel value of image Z after binary conversion treatment；
P indicates the original pixel value in average value image z；
TthresholdIndicate the binary-state threshold of setting, TthresholdValue be 40.
It can be obtained by Fig. 3, the sum of pixel value of image Z after binary conversion treatment is 25755.
The alert thresholds K that 6th step, the present embodiment are set is 2295, all pixels value in the image Z after binary conversion treatment
The sum of be 25755, i.e., the sum of all pixels value is more than the alert thresholds K of setting in the image Z after the described binary conversion treatment, then sentences
Pipeline liquid leakage has occurred for the fixed video-frequency band to be measured, and sends out alarm to cabin channel worker.
The de-jitter is：A pixel P in gray level image is taken, the pixel P is in gray level image
Position coordinates are (a, b)；It is (a, b), the pixel that size is 3 × 3 that center is taken in gray level image y when pipeline does not leak
Block P1；The pixel p and each pixel in the block of pixels P1 are subtracted each other, the matrix P2 that size is 3 × 3 is obtained；Take square
The best match of pixel P in gray level image y when absolute value minimum value position does not leak as pipeline in battle array P2
The position of point；In gray level image y when the value of absolute value minimum in matrix P2 is not leaked as pixel P and pipeline
The difference of optimal match point.
Rest of pixels point in the gray level image is all done into above-mentioned processing, obtains error image.
Embodiment 2
A kind of pipeline liquid leakage monitoring method based on image procossing.The specific step of monitoring method described in the present embodiment
Suddenly it is：
The first step intercepts color RGB image Y when a frame cabin pipeline does not leak from video to be detected；Using
Color RGB image Y when gray level image algorithm does not leak the pipeline carries out greyscale transformation, obtains as shown in Figure 4
Gray level image y when pipeline does not leak；The gray level image algorithm is：
Gray=R × 0.299+G × 0.587+B × 0.114 (1)
In formula (1)：R indicates the red component of colour RGB pictures；
G indicates the green component of colour RGB pictures；
B indicates the blue component of colour RGB pictures.
Second step, from the 1st frame RGB color image in the video to be detected, take every 300 frame RGB color image to be
One video-frequency band to be detected detects each video-frequency band to be detected one by one since first video-frequency band to be detected, each to be detected
The detection method of video-frequency band is that third walks the~the five step.
It is image to be detected that every 10 frame RGB color image, which takes a frame RGB color image, in the video-frequency band to be detected, is had
30 frame image to be detected.
Described 30 frame image to be detected is switched to gray level image by third step with formula (1), obtains gray level image set to be detected
Q1, Fig. 5 are the piece image in the gray level image set Q1 to be detected.
4th step, the gray level image y and gray level image set Q1 to be measured when the pipeline to that liquid leakage do not occur
In every piece image do de-jitter, obtain 30 width error images；Then the 30 width error image is averaged, is obtained
To 1 width average value image z.
5th step carries out binary conversion treatment to the average value image z, after obtaining binary conversion treatment as shown in FIG. 6
Image Z；The algorithm of the binary conversion treatment is：
In formula (2)：Value indicates the pixel value of image Z after binary conversion treatment；
P indicates the original pixel value in average value image z；
TthresholdIndicate the binary-state threshold of setting, TthresholdValue be 100.
It can be obtained by Fig. 6, the sum of pixel value of image Z after binary conversion treatment is 765.
The alert thresholds K that 6th step, the present embodiment are set is 5100.All pixels value in image Z after binary conversion treatment
The sum of be 765, i.e., the sum of all pixels value is less than threshold k in the image Z after the described binary conversion treatment, then judges described to be measured regard
Pipeline liquid leakage does not occur for frequency range.
The de-jitter is：A pixel P in gray level image is taken, the pixel P is in gray level image
Position coordinates are (a, b)；It is (a, b), the pixel that size is 5 × 5 that center is taken in gray level image y when pipeline does not leak
Block P1；The pixel p and each pixel in the block of pixels P1 are subtracted each other, the matrix P2 that size is 5 × 5 is obtained；Take square
Best match of the absolute value minimum value institute position as pixel P in the gray level image y when pipeline does not leak in battle array P2
The position of point；In gray level image y when the value of absolute value minimum in matrix P2 is not leaked as pixel P and pipeline
The difference of optimal match point.
Rest of pixels point in described gray level image is all done into above-mentioned processing, obtains error image.
Present embodiment has the following advantages that compared with prior art：
The pipeline liquid leakage monitoring method based on image procossing that present embodiment is proposed uses simply, as long as
It asks staff to calibrate the state that pipeline liquid leakage does not occur, can quickly detect and pipeline liquid leakage whether occur.
Present embodiment uses de-jitter technology, high to the monitoring accuracy of pipeline liquid leakage, especially
There is preferable robustness for the fine jitter that video camera generates when external environment is unstable, it is adaptable.
It is special that image when the determination method that present embodiment is used is not necessarily to that pipeline occurs liquid leakage carries out
Training and study, greatly reduce complexity, shorten the duration of processing.
Present embodiment is capable of the state of independent judgment single width testing image, meets the requirement of on-line real-time measuremen,
It can find that pipeline liquid leaks in a short time, and make alarm, to increase the reliability of detection, can greatly reduce people
Work cost.
Therefore, present embodiment has that precision is high, method is simple, processing procedure is short, adaptable and can realize
The characteristics of line monitors in real time.
Claims (2)
1. a kind of pipeline liquid leakage monitoring method based on image procossing, it is characterised in that the pipeline liquid leakage monitoring side
The specific steps of method：
The first step intercepts color RGB image Y when a frame pipeline does not leak from video to be detected；Using gray level image
Color RGB image Y when algorithm does not leak the pipeline carries out greyscale transformation, obtains ash when pipeline does not leak
Spend image y；The gray level image algorithm is：
Gray=R × 0.299+G × 0.587+B × 0.114 (1)
In formula (1)：R indicates the red component of colour RGB pictures,
G indicates the green component of colour RGB pictures,
B indicates the blue component of colour RGB pictures；
Second step, from the 1st frame RGB color image in the video to be detected, take every L frames RGB color image be one wait for
Video-frequency band is detected, detects each video-frequency band to be detected one by one since first video-frequency band to be detected, each video-frequency band to be detected
Detection method be third walk the~the five step；
It takes a frame RGB color image for image to be detected per n frame RGB color images in the video-frequency band to be detected, there are m frames to wait for
Detection image, then m × n=L, wherein：N is 2~100 natural numbers, the natural number that m is 10~1000；
Described m frames image to be detected is switched to gray level image by third step with formula (1), obtains gray level image set Q1 to be detected；
In 4th step, the gray level image y and gray level image set Q1 to be detected when the pipeline to that liquid leakage do not occur
Every piece image do de-jitter, obtain m width error images；Then the m width error image is averaged, obtains 1
Width average value image z；
5th step carries out binary conversion treatment to the average value image z, obtains the image Z after binary conversion treatment；The binaryzation
The algorithm of processing is：
In formula (2)：Value indicates the pixel value of image Z after binary conversion treatment,
P indicates the original pixel value in average value image z,
TthresholdIndicate the binary-state threshold of setting, TthresholdFor 10~200 natural number；
If the sum of all pixels value is less than the alert thresholds K of setting in the image Z after the 6th step, binary conversion treatment, described to wait for
Pipeline liquid leakage does not occur for detection video-frequency band；If the sum of all pixels value is more than setting in the image Z after binary conversion treatment
Alert thresholds K, then the video-frequency band to be detected pipeline liquid leakage has occurred；
Natural numbers of the alert thresholds K set as 2550~100000.
2. according to the pipeline liquid leakage monitoring method based on image procossing described in claim 1, it is characterised in that the debounce
Dynamic processing is：It is (a, b) to take the position coordinates of pixel P, the pixel P in gray level image in gray level image；
It is (a, b) that center is taken in gray level image y when pipeline does not leak, and size is the block of pixels P1 of M × M；The pixel
Point p subtracts each other with each pixel in the block of pixels P1, obtains the matrix P2 that size is M × M；Take absolute value in matrix P2 minimum
The position of the optimal match point of pixel P in gray level image y when value position does not leak as pipeline；Matrix P2
The difference of the optimal match point in gray level image y when the value of middle absolute value minimum does not leak as pixel P and pipeline；
Rest of pixels point in the gray level image is all done into above-mentioned processing, obtains error image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710237423.9A CN107013811B (en) | 2017-04-12 | 2017-04-12 | A kind of pipeline liquid leakage monitoring method based on image procossing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710237423.9A CN107013811B (en) | 2017-04-12 | 2017-04-12 | A kind of pipeline liquid leakage monitoring method based on image procossing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107013811A CN107013811A (en) | 2017-08-04 |
| CN107013811B true CN107013811B (en) | 2018-10-09 |
Family
ID=59446589
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710237423.9A Expired - Fee Related CN107013811B (en) | 2017-04-12 | 2017-04-12 | A kind of pipeline liquid leakage monitoring method based on image procossing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107013811B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109035249B (en) * | 2018-09-10 | 2021-08-24 | 东北大学 | Pipeline fault parallel global threshold detection method based on image processing |
| CN111982415A (en) * | 2019-05-24 | 2020-11-24 | 杭州海康威视数字技术股份有限公司 | Pipeline leakage detection method and device |
| CN110274160B (en) * | 2019-06-13 | 2021-05-11 | 咏峰（大连）科技有限公司 | Pipeline inspection system based on infrared and visible light fusion image |
| CN110414478A (en) * | 2019-08-08 | 2019-11-05 | 东莞德福得精密五金制品有限公司 | The contingency liquefied gas leak supervision method of the non-Application inductor of artificial intelligence cloud computing |
| CN110470669B (en) * | 2019-08-23 | 2020-05-26 | 吉林大学 | Leakage detection method and system for underwater pipeline and related device |
| CN112098055B (en) * | 2020-07-07 | 2021-03-23 | 青岛大学附属医院 | System and method for detecting blood transfusion pipeline shaking |
| CN113606502B (en) * | 2021-07-16 | 2023-03-24 | 青岛新奥燃气设施开发有限公司 | Method for judging whether operator performs pipeline air leakage detection based on machine vision |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006329383A (en) * | 2005-05-30 | 2006-12-07 | Sekisui Chem Co Ltd | System and method for diagnosing pipe conduit |
| CN101539241A (en) * | 2009-05-07 | 2009-09-23 | 北京航空航天大学 | Hierarchical multi-source data fusion method for pipeline linkage monitoring network |
| CN102927448A (en) * | 2012-09-25 | 2013-02-13 | 北京声迅电子股份有限公司 | Undamaged detection method for pipeline |
| WO2015193043A1 (en) * | 2014-06-16 | 2015-12-23 | Agt International Gmbh | Flash flooding detection system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7697026B2 (en) * | 2004-03-16 | 2010-04-13 | 3Vr Security, Inc. | Pipeline architecture for analyzing multiple video streams |
-
2017
- 2017-04-12 CN CN201710237423.9A patent/CN107013811B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006329383A (en) * | 2005-05-30 | 2006-12-07 | Sekisui Chem Co Ltd | System and method for diagnosing pipe conduit |
| CN101539241A (en) * | 2009-05-07 | 2009-09-23 | 北京航空航天大学 | Hierarchical multi-source data fusion method for pipeline linkage monitoring network |
| CN102927448A (en) * | 2012-09-25 | 2013-02-13 | 北京声迅电子股份有限公司 | Undamaged detection method for pipeline |
| WO2015193043A1 (en) * | 2014-06-16 | 2015-12-23 | Agt International Gmbh | Flash flooding detection system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107013811A (en) | 2017-08-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107013811B (en) | A kind of pipeline liquid leakage monitoring method based on image procossing | |
| US9805458B2 (en) | Method and system for detecting defective pixels and screen imperfections of a mobile device | |
| CN103702111B (en) | A kind of method detecting camera video color cast | |
| JP6325520B2 (en) | Unevenness inspection system, unevenness inspection method, and unevenness inspection program | |
| TW200723170A (en) | Defect detecting device, image sensor device, image sensor module, image processing device, digital image quality tester, and defect detecting method | |
| KR100396449B1 (en) | Image defect detection apparatus and image defect detection method | |
| CN109919933B (en) | VR device, picture detection method and device thereof, and computer-readable storage medium | |
| CN105911724B (en) | Determine the method and apparatus of the intensity of illumination for detection and optical detecting method and device | |
| CN103927749A (en) | Image processing method and device and automatic optical detector | |
| CN109461156B (en) | Threaded sealing plug assembly detection method based on vision | |
| JP2018179698A (en) | Sheet inspection device | |
| KR100842616B1 (en) | Method and Apparatus For Detecting Flat Panel Display By Vision Model | |
| US20150356896A1 (en) | Apparatus and method for image analysis and image display | |
| CN110553151B (en) | Pipeline leakage monitoring method and system | |
| JP2003298949A (en) | Method for detecting flicker defect, video correction method, and solid-state image pickup apparatus | |
| KR20100001914A (en) | Picture quality assessment method and picture quality assessment apparatus for display device | |
| CN105628195A (en) | Light source brightness detecting system and method | |
| CN103200349A (en) | Scanned image color cast automatic detection method | |
| TWI590196B (en) | Method for detecting of liquid | |
| CN114092437B (en) | Transformer leakage oil detection method | |
| KR101946581B1 (en) | Panel Inspection Method | |
| CN109509185A (en) | The detection method and equipment of bottle cap, production line, computer equipment, storage medium | |
| JP2019117177A5 (en) | ||
| TWI470207B (en) | A method of building up gray-scale transform function, a panel testing method and an automated panel testing system | |
| KR20150009842A (en) | System for testing camera module centering and method for testing camera module centering using the same |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20181009 |