CN111524101A - Electronic screen defect detection method based on machine vision technology - Google Patents
Electronic screen defect detection method based on machine vision technology Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 claims abstract description 24
- 238000007781 pre-processing Methods 0.000 claims abstract description 14
- 238000001914 filtration Methods 0.000 claims description 15
- 239000000428 dust Substances 0.000 claims description 14
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- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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Abstract
The invention discloses an electronic screen defect detection method based on a machine vision technology, which comprises the following steps: collecting an electronic screen image, fixing two ends of an electronic product by using a clamp fixed on a measuring table, keeping the electronic product horizontal, photographing the electronic screen in a lightening state along the vertical direction by using a CCD (charge coupled device) camera, digitizing the collected electronic product image and transmitting the digitized electronic product image to related software of a computer for next image preprocessing; preprocessing the digitized image in the first step; and (3) image identification and classification, namely, classifying the preprocessed image into defect classification, namely, defective point defect, defective line defect and block defect, and feeding back the classification result. Based on the machine vision technology, the invention can carry out image acquisition and pretreatment on the electronic screen, further analyze the defect classification of the electronic screen and carry out feedback; the method and the device are high in detection efficiency, stable in detection accuracy and low in detection cost, and are suitable for large-scale detection in industrial production.
Description
Technical Field
The invention belongs to the technical field of electronic screen defect detection, and particularly relates to an electronic screen defect detection method based on a machine vision technology.
Background
With the increasing prosperity and fierce competition of the electronic product market, the production mode of high-quality and high-efficiency electronic products is favored by the market. As electronic screen displays are developed in the direction of large size, high resolution, and lightness, the probability of various defects of the electronic screen is greatly increased. Therefore, in the production process of the electronic screen, strict defect detection is required to ensure the production quality.
The detection of current electronic screen defect mainly relies on traditional artifical viewing to go on, detects the restriction that the main part received subjective and objective aspect defect, and this kind of detection means not only appears lou examining, the false retrieval scheduling problem easily, and reliability and stability are difficult to guarantee, and detection efficiency is not high moreover, the real-time is poor, and the cost that detects the needs is also very high. For the industrial production in large quantities today, the manual viewing method cannot meet the requirement of the industrial production.
Disclosure of Invention
Aiming at the defects in the technology, the invention provides an electronic screen defect detection method based on a machine vision technology.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an electronic screen defect detection method based on a machine vision technology comprises the following steps:
step one, collecting an electronic screen image: fixing two ends of the electronic product by using a clamp fixed on a measuring table, keeping the electronic product horizontal, photographing an electronic screen in a lighting state along the vertical direction by using a CCD (charge coupled device) camera, digitizing the acquired image of the electronic product and transmitting the digitized image to related software of a computer for next image preprocessing;
step two, preprocessing the digitized image in the step one;
step three, image recognition and classification: and classifying the preprocessed image into defect classification, namely classifying the preprocessed image into a dead pixel defect, a dead line defect and a block defect, and feeding back a classification result.
Preferably, the electronic screen image capturing environment in the step one is as follows: the collection environment is set as a dark room and the electronic screen is adjusted to a state of lighting without displaying any pattern.
Preferably, the preprocessing mainly includes image cutting, image filtering and image segmentation.
Preferably, the image cropping process is as follows: the method comprises the steps of initially acquiring an ROI by utilizing an automatic threshold, finishing image rotation correction according to the inclination angle of an ROI area, extracting edge information points by combining four corner point information of the ROI area with the change of image polarity, fitting straight lines by utilizing the edge information points to form a cutting rectangle, and finishing cutting of an image.
Preferably, the image filtering process is as follows: the Gaussian filtered image is used as an initial image of mean filtering to reduce the influence of texture and brightness unevenness, and then the median filtering is used for completing the combined filtering processing of the image.
Preferably, the image segmentation process is as follows: and a joint detection mode combining local threshold segmentation and regional contrast is utilized. Firstly, dividing a local threshold to preliminarily extract defects, then judging whether the defects are reasonable or not through local contrast, and finally extracting the defects.
Preferably, the electronic screen defect detection method based on the machine vision technology further comprises a dust removal step, namely before the electronic screen is not lightened, a system light source is turned on, an image is collected, and then the position and the area of the dust are obtained; and jointly comparing the dust area with the defect area to eliminate the influence of dust on the image detection result.
Preferably, the pretreatment process is as follows: firstly, binarization and edge detection are carried out to determine the position of an electronic screen area, secondly, geometric correction is adopted to keep the target area horizontal, which is beneficial to carrying out target extraction operation, and finally, color space conversion is carried out to improve the contrast ratio between the screen defect and the periphery, so that the defect detection is carried out later.
Compared with the prior art, the invention has the beneficial effects that: the electronic screen defect detection method based on the machine vision technology provided by the invention is based on the machine vision technology, can be used for carrying out image acquisition and pretreatment on an electronic screen, further analyzing the defect classification of the electronic screen and feeding back the defect classification; the method and the device are high in detection efficiency, stable in detection accuracy and low in detection cost, and are suitable for large-scale detection in industrial production.
Drawings
FIG. 1 is a flow chart of the detection method of the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1, the present invention provides a method for detecting defects of an electronic screen based on a machine vision technology, comprising the following steps:
step one, collecting an electronic screen image: fixing two ends of an electronic product (such as a mobile phone, a tablet personal computer, an intelligent watch and the like) by using a clamp fixed on a measuring table, keeping the electronic product horizontal, photographing an electronic screen in a lighting state along the vertical direction by using a CCD (charge coupled device) camera, digitizing the acquired image of the electronic product and transmitting the digitized image to related software of a computer for next image preprocessing;
step two, preprocessing the digitized image in the step one;
step three, image recognition and classification: and classifying the preprocessed image into defect classification, namely classifying the preprocessed image into a dead pixel defect, a dead line defect and a block defect, and feeding back a classification result.
As an embodiment of the present invention, in the first step, the image capturing environment of the electronic screen is as follows: the collection environment is set as a dark room and the electronic screen is adjusted to a state of lighting without displaying any pattern.
As an embodiment of the invention, the acquired electronic screen image must be preprocessed before the defect detection, so that the interference of irrelevant factors during the screen defect detection is reduced, and the detection efficiency of the algorithm is improved. The preprocessing mainly comprises image shearing, image filtering and image segmentation.
Wherein the image cropping process is as follows: the method comprises the steps of initially acquiring an ROI by utilizing an automatic threshold, finishing image rotation correction according to the inclination angle of an ROI area, extracting edge information points by combining four corner point information of the ROI area with the change of image polarity, fitting straight lines by utilizing the edge information points to form a cutting rectangle, and finishing cutting of an image.
Wherein the image filtering process is as follows: the Gaussian filtered image is used as an initial image of mean filtering to reduce the influence of texture and brightness unevenness, and then the median filtering is used for completing the combined filtering processing of the image.
Wherein the image segmentation process is as follows: and a joint detection mode combining local threshold segmentation and regional contrast is utilized. Firstly, dividing a local threshold to preliminarily extract defects, then judging whether the defects are reasonable or not through local contrast, and finally extracting the defects.
As an embodiment of the present invention, the existence of dust in the darkroom may cause small-area defects (e.g., dead spots) and inevitably cause certain interference to the detection, and non-darkroom images under the same condition should be collected at the same time as a reference for the subsequent dust removal operation.
Therefore, the invention also comprises a dust removal step, namely before the electronic screen is not lightened, the system light source is firstly turned on, an image is collected, and then the position and the area of the dust are obtained; and jointly comparing the dust area with the defect area to eliminate the influence of dust on the image detection result.
As an embodiment of the present invention, the preprocessing process includes: firstly, binarization and edge detection are carried out to determine the position of an electronic screen area, secondly, geometric correction is adopted to keep the target area horizontal, which is beneficial to carrying out target extraction operation, and finally, color space conversion is carried out to improve the contrast ratio between the screen defect and the periphery, so that the defect detection is carried out later.
Through the preprocessing operation, the target area to be detected is successfully extracted, an electronic screen image is obtained, and the detection efficiency of the algorithm is greatly improved.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (8)
1. A defect detection method of an electronic screen based on a machine vision technology is characterized by comprising the following steps:
step one, collecting an electronic screen image: fixing two ends of the electronic product by using a clamp fixed on a measuring table, keeping the electronic product horizontal, photographing an electronic screen in a lighting state along the vertical direction by using a CCD (charge coupled device) camera, digitizing the acquired image of the electronic product and transmitting the digitized image to related software of a computer for next image preprocessing;
step two, preprocessing the digitized image in the step one;
step three, image recognition and classification: and classifying the preprocessed image into defect classification, namely classifying the preprocessed image into a dead pixel defect, a dead line defect and a block defect, and feeding back a classification result.
2. The method for detecting defects of an electronic screen based on machine vision technology as claimed in claim 1, wherein the image capturing environment of the electronic screen is set as a dark room in the first step, and the electronic screen is adjusted to a state of lighting without displaying any pattern.
3. The method of claim 1, wherein the pre-processing comprises mainly image cropping, image filtering, and image segmentation.
4. The method for detecting the defects of the electronic screen based on the machine vision technology as claimed in claim 3, wherein the image cutting process is as follows: the method comprises the steps of initially acquiring an ROI by utilizing an automatic threshold, finishing image rotation correction according to the inclination angle of an ROI area, extracting edge information points by combining four corner point information of the ROI area with the change of image polarity, fitting straight lines by utilizing the edge information points to form a cutting rectangle, and finishing cutting of an image.
5. The method for detecting defects of an electronic screen based on a machine vision technology as claimed in claim 3, wherein the image filtering process is as follows: the Gaussian filtered image is used as an initial image of mean filtering to reduce the influence of texture and brightness unevenness, and then the median filtering is used for completing the combined filtering processing of the image.
6. The method for detecting the defects of the electronic screen based on the machine vision technology as claimed in claim 3, wherein the image segmentation process comprises the following steps: a joint detection mode combining local threshold segmentation and regional contrast is utilized; firstly, dividing a local threshold to preliminarily extract defects, then judging whether the defects are reasonable or not through local contrast, and finally extracting the defects.
7. The electronic screen defect detection method based on the machine vision technology as claimed in claim 1, characterized by further comprising a dust removal step, namely, before the electronic screen is not lighted, a system light source is turned on, an image is collected, and then the position and the area of the dust are obtained; and jointly comparing the dust area with the defect area to eliminate the influence of dust on the image detection result.
8. The electronic screen defect detecting method based on machine vision technology as claimed in claim 1, wherein said preprocessing process is: firstly, binarization and edge detection are carried out to determine the position of an electronic screen area, secondly, geometric correction is adopted to keep the target area horizontal, which is beneficial to carrying out target extraction operation, and finally, color space conversion is carried out to improve the contrast ratio between the screen defect and the periphery, so that the defect detection is carried out later.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111815630A (en) * | 2020-08-28 | 2020-10-23 | 歌尔股份有限公司 | Defect detection method and device for LCD screen |
| CN112394064A (en) * | 2020-10-22 | 2021-02-23 | 惠州高视科技有限公司 | Point-line measuring method for screen defect detection |
| CN112967258A (en) * | 2021-03-15 | 2021-06-15 | 歌尔科技有限公司 | Display defect detection method and device for watch and computer readable storage medium |
| CN113012137A (en) * | 2021-03-24 | 2021-06-22 | 滁州惠科光电科技有限公司 | Panel defect inspection method, system, terminal device and storage medium |
| CN114663356A (en) * | 2022-02-28 | 2022-06-24 | 东莞市德普特电子有限公司 | Method and system for distinguishing interior dark spots and surface dust during detection of mobile phone screen module |
| CN117115171A (en) * | 2023-10-25 | 2023-11-24 | 苏州视达讯远电子科技有限公司 | Slight bright point defect detection method applied to subway LCD display screen |
| CN117911409A (en) * | 2024-03-19 | 2024-04-19 | 深圳市酷童小样科技有限公司 | Mobile phone screen bad line defect diagnosis method based on machine vision |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111815630A (en) * | 2020-08-28 | 2020-10-23 | 歌尔股份有限公司 | Defect detection method and device for LCD screen |
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| CN112967258A (en) * | 2021-03-15 | 2021-06-15 | 歌尔科技有限公司 | Display defect detection method and device for watch and computer readable storage medium |
| CN112967258B (en) * | 2021-03-15 | 2023-01-24 | 歌尔科技有限公司 | Display defect detection method and device for watch and computer readable storage medium |
| CN113012137A (en) * | 2021-03-24 | 2021-06-22 | 滁州惠科光电科技有限公司 | Panel defect inspection method, system, terminal device and storage medium |
| CN114663356A (en) * | 2022-02-28 | 2022-06-24 | 东莞市德普特电子有限公司 | Method and system for distinguishing interior dark spots and surface dust during detection of mobile phone screen module |
| CN117115171A (en) * | 2023-10-25 | 2023-11-24 | 苏州视达讯远电子科技有限公司 | Slight bright point defect detection method applied to subway LCD display screen |
| CN117115171B (en) * | 2023-10-25 | 2024-01-26 | 苏州视达讯远电子科技有限公司 | Slight bright point defect detection method applied to subway LCD display screen |
| CN117911409A (en) * | 2024-03-19 | 2024-04-19 | 深圳市酷童小样科技有限公司 | Mobile phone screen bad line defect diagnosis method based on machine vision |
| CN117911409B (en) * | 2024-03-19 | 2024-05-31 | 深圳市酷童小样科技有限公司 | Mobile phone screen bad line defect diagnosis method based on machine vision |
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Application publication date: 20200811 |