CN210604434U - Online detection device for typical defects of curved glass of mobile phone - Google Patents
Online detection device for typical defects of curved glass of mobile phone Download PDFInfo
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Abstract
The utility model discloses a typical defect on-line measuring device of cell-phone curved surface glass, include: the workpiece transmission device comprises a transmission guide rail, a plurality of transparent object stages, a motion control card, a motor and a rotating table; a plane part detection device, a curved edge part detection device and an R angle part detection device which respectively form three stations are sequentially arranged along the transmission direction of the workpiece transmission device; the plane part detection device comprises an object stage position detection device, a first computer, a line scanning camera with a lens, a linear coaxial light source, a light source controller and an image acquisition card; the detection device for the curved edge part comprises a first area array camera provided with a second lens, a strip light source, a light source controller, a second image acquisition card, a second computer and an object stage position detection device. The utility model discloses can real-time on-line measuring cell-phone curved surface glass's typical defects such as mar, burn, collapse limit, pockmark have quick, high-efficient, accurate, stable characteristics.
Description
Technical Field
The utility model relates to an automatic change the detection area, especially relate to a typical defect on-line measuring device of cell-phone curved surface glass.
Background
In recent years, the internet technology is rapidly developed, and the smart phone is taken as a first large internet terminal device, integrates internet surfing, entertainment and office into a whole and is favored by more and more consumers. Each large mobile phone manufacturer gradually improves functions and performance, and starts to adopt curved glass with better texture and holding feeling as a mobile phone screen or a mobile phone rear shell. The glass rear shell is beneficial to heat dissipation of the mobile phone, does not inhibit signals of the mobile phone, and is more beneficial to arrival of the 5G era and realization of a wireless charging function. However, the design of curved glass on mobile phones is still a huge attempt and innovation, because the current technology can not solve all the problems in production. The production of the curved glass needs the following operation flows: cutting, hot bending, cooling, CNC processing and the like are influenced by technical conditions, production environment or environmental factors, and defects such as scratches, pits, bubbles, edge breakage, burns, scratches, indentations and the like can occur in each processing procedure. The existence of defects seriously affects the mechanical rigidity of the curved glass and reduces the use value of products. Therefore, it is very important to detect the defects of curved glass in the production process.
The traditional detection method is manual visual detection, and whether the defects exist or not is judged according to subjective visual perception of quality testing personnel. Currently, this method is adopted by most cell phone manufacturers. The method has low efficiency and high detection cost, is greatly influenced by subjective factors, has inconsistent understanding degree of detection personnel to the detection standard, has the final detection result related to experience and proficiency of quality testing personnel, can cause visual fatigue after long-time work, causes false detection and missed detection of products, and is not beneficial to the current situation of the demand of short supply. In addition, the detection result and data detected by the naked eyes are not easy to store, so that the subsequent data result is not convenient to search and analyze, and the detected data result is difficult to guide the subsequent production.
SUMMERY OF THE UTILITY MODEL
The utility model discloses to the drawback in the artifical naked eye detects, provide a high efficiency, accurate, quick, stable cell-phone curved surface glass typical defect on-line measuring device. The device is divided into a plane part, a curved edge part and an R angle part to acquire images of the curved glass of the mobile phone, quickly identifies the curved glass based on a convolutional neural network, and judges whether the workpiece has defects and defect types, such as typical defects of scratches, pits, edge breakage, burns and the like. The device and the method are suitable for online quality detection of mass mobile phone curved glass.
For realizing the purpose of the utility model, the utility model discloses a following technical scheme realizes:
the utility model provides a typical defect on-line measuring device of cell-phone curved surface glass, includes:
the workpiece transmission device is used for transmitting a curved glass workpiece to be detected and comprises a transmission guide rail, a plurality of transparent object stages, a motion control card, a motor and a rotating table, wherein the transmission guide rail is in driving connection with the motion control card and the motor, the rotating table is in driving connection with the transparent object stages, and the transparent object stages are arranged on the transmission guide rail at intervals;
a plane part detection device, a curved edge part detection device and an R angle part detection device which respectively form a plane detection station, a curved edge detection station and an R angle detection station are sequentially arranged along the transmission direction of the workpiece transmission device,
the plane part detection device comprises an object stage position detection device, a first computer, a line scanning camera with a lens, a linear coaxial light source, a light source controller and an image acquisition card, wherein the light source controller is used for adjusting the brightness of the linear coaxial light source, and the linear coaxial light source irradiates the plane part of the curved glass workpiece; the image acquisition card is used for acquiring the image of the plane part of the curved glass workpiece acquired by the line scanning camera; the object stage position detection device is used for detecting the current position of the transparent object stage; the first computer is respectively connected with the image acquisition card, the motion control card and the motor, the rotary table and the objective table position detection device through data lines and is used for finishing the control of plane part detection and the online defect identification of the plane part;
the curved edge part detection device comprises a first area array camera provided with a second lens, a strip-shaped light source, a light source controller, a second image acquisition card, a second computer and an object stage position detection device, wherein the light source controller is used for controlling the strip-shaped light source, the strip-shaped light source irradiates the curved edge part of the curved glass workpiece, and the second image acquisition card is used for acquiring the image of the curved edge part of the curved glass workpiece, which is acquired by the first area array camera; the object stage position detection device is used for detecting the current position of the transparent object stage; the second computer is respectively connected with the motor motion control card, the second image acquisition card, the objective table position detection device and the rotary table through data lines and is used for finishing control of detection of the curved edge part and online defect identification of the curved edge part;
the R angle part detection device comprises a third computer, an object stage position detection device, a second surface array camera provided with a telecentric lens, a parallel light source, a light source controller and a third image acquisition card, wherein the light source controller controls the brightness of the parallel light source, and the third image acquisition card is used for acquiring an R angle part image of the curved glass workpiece acquired by the second surface array camera; the object stage position detection device is used for detecting the current position of the transparent object stage; and the third computer is respectively connected with the motion control card and the motor, the third image acquisition card, the object stage position detection device and the rotating platform through data lines and is used for finishing the control of the detection station of the R angle part and the online defect identification of the R angle part.
Furthermore, the line scanning camera vertically faces the plane part of the curved glass workpiece, the line scanning field of the line scanning camera is larger than the width of the curved glass sample, the light rays emitted by the linear coaxial light source form a certain included angle with the line scanning camera, and the light bars have uniform brightness and can cover the curved glass workpiece.
Furthermore, first area array camera is high resolution CMOS area array camera, and the camera axis passes through the curvature center of the curved edge of curved surface glass work piece and forms certain contained angle α with the horizontal direction, and two bar light source symmetries set up in first area array camera both sides and its angle of polishing β guarantee to fall into after the curved edge reflection the target surface within range of first area array camera is in order to form clear image.
Furthermore, the parallel light source and the second area array camera vertically face the R angle part of the curved glass workpiece, the second area array camera is a high-resolution area array camera, and images are acquired through the telecentric lens in a mode that the parallel light source is used for lighting backlight.
Furthermore, the stage position detection device adopts photoelectric sensors which are respectively arranged at corresponding positions on the conveying guide rail.
The utility model provides a cell-phone curved surface glass typical defect on-line measuring device solves and has overcome the drawback in artifical visual inspection, can be fast, high-efficient, accurate, steadily real-time on-line measuring cell-phone curved surface glass in mar, burn, collapse typical defects such as limit, pockmark, concrete advantage includes:
(1) the curved glass is detected by three detection stations, so that detection blind areas are avoided, and meanwhile, the detection efficiency is improved by multi-station assembly line detection.
(2) The objective and accurate detection result can be ensured, and the non-contact detection can avoid secondary pollution or damage to the curved glass.
(3) The method can realize real-time online detection and is suitable for detection of large-batch samples.
(4) Aiming at curved glass samples of different models, the positions and postures of the camera and the light source can be adjusted, and detection of different mobile phone curved glass is realized.
Drawings
Fig. 1 is a general schematic diagram of the typical defect on-line detection device for mobile phone curved glass of the present invention.
Fig. 2 is the structure schematic diagram of the plane part detection station of the present invention.
Fig. 3 is a schematic structural view of the curved edge detection station of the present invention.
FIG. 4 shows the positional relationship between the camera target surface, the curved edge glass, and the light source angle in the curved edge detection station.
Fig. 5 is a schematic structural view of the R-corner detection station of the present invention.
The numbers in the figures illustrate the following:
1-a transfer rail; 2-a transparent stage; 3-curved glass workpiece; 4-a motor; 5-line scan camera; 6-a first lens; 7-linear coaxial light source; 8-a light source controller; 9-a first image acquisition card; 10-a photosensor; 11-a first computer; 12-a rotating table; 13-a first area-array camera; 14-a second lens; 15-a bar light source; 16-a second image acquisition card; 17-a second computer; 18-a second area-array camera; 19-a telecentric lens; 20-a collimated light source; 21-a third image acquisition card; 22-third computer.
Detailed Description
For a better understanding of the present invention, the present invention will be described in further detail with reference to the following embodiments.
Examples
As shown in fig. 1, an online detection device for typical defects of curved glass of a mobile phone includes:
the workpiece conveying device is used for conveying a curved glass workpiece 3 to be detected and comprises a conveying guide rail 1, a plurality of transparent object stages 2, a motion control card and motor 4 and a rotating table 12, wherein the conveying guide rail 1 is in driving connection with the motion control card and the motor 4, the rotating table 12 is in driving connection with the transparent object stages 2, and the transparent object stages 2 are arranged on the conveying guide rail 1 at intervals;
a plane part detection device, a curved edge part detection device and an R angle part detection device which respectively form a plane detection station, a curved edge detection station and an R angle detection station are sequentially arranged along the transmission direction of the workpiece transmission device,
as shown in fig. 2, the plane part detecting device comprises a photoelectric sensor 10, a first computer 11, a line scan camera 5 with a lens 6, a linear coaxial light source 7, a light source controller 8 and a first image acquisition card 9, wherein the light source controller 8 is used for adjusting the brightness of the linear coaxial light source 7, and the linear coaxial light source 7 irradiates the plane part of the curved glass workpiece 3; the first image acquisition card 9 is used for acquiring the image of the plane part of the curved glass workpiece 3 acquired by the line scanning camera 5; the photoelectric sensor 10 is arranged at a corresponding position on the transmission guide rail 1 and is used for detecting the current position of the transparent object stage 2; the linear scanning camera 5 vertically faces the plane part of the curved glass workpiece 3, the linear scanning field of the linear scanning camera is larger than the width of the curved glass sample, the light rays emitted by the linear coaxial light source 7 form a certain included angle with the linear scanning camera 5, and the light bars have uniform brightness and can cover the curved glass workpiece 3. The first computer 11 is connected with the first image acquisition card 9, the motion control card and motor 4, the rotary table 12 and the photoelectric sensor 10 through data lines respectively, and is used for finishing the control of the detection of the plane part and the online defect identification of the plane part.
As shown in fig. 3, the curved edge portion detecting device includes a first area array camera 13 provided with a second lens 14, a strip light source 15, a light source controller 8, a second image collecting card 16, a second computer 17, and a photoelectric sensor 10, wherein the light source controller 8 is configured to control the strip light source 15, the strip light source 15 irradiates on the curved edge portion of the curved glass workpiece 3, and the second image collecting card 16 is configured to collect an image of the curved edge portion of the curved glass workpiece 3 acquired by the first area array camera 13; the photoelectric sensor 10 is arranged at a corresponding position on the transmission guide rail 1 and is used for detecting the current position of the transparent object stage 2; the second computer 17 is connected to the motor motion control card 4, the second image acquisition card 16, the photoelectric sensor 10 and the rotary table 12 through data lines, and is used for completing control of detection of the curved edge portion and online defect identification of the curved edge portion.
As shown in FIG. 4, the first area-array camera 13 is a high-resolution CMOS area-array camera, the axis of the camera passes through the curvature center of the curved edge of the curved glass workpiece 3 and forms a certain included angle α with the horizontal direction, the two strip-shaped light sources 15 are symmetrically arranged on both sides of the first area-array camera 13 and the light-striking angles β thereof ensure that the strip-shaped light sources fall into the range of the target surface of the first area-array camera 13 after being reflected by the curved edge to form a clear image, the light-striking angles β are the included angles between the light sources and the axis of the camera, the values of α and β depend on the radius of the curved edge of the curved glass workpiece 3 and the camera parameters, A, B is the critical point of the imaging field of view of the camera, theta is 1/2 field angle, H isWidth of machine target surface, K1、K2、K3、K4Representing the slope of the incident and reflected rays, respectively.
As shown in fig. 5, the R-angle portion detecting device includes a third computer 22, a photoelectric sensor 10, a second area-array camera 18 with a telecentric lens 19, a parallel light source 20, a light source controller 8 and a third image acquisition card 21, wherein the light source controller 8 controls the brightness of the parallel light source 20, the parallel light source 20 and the second area-array camera 18 vertically face the R-angle portion of the curved glass workpiece 3, and the third image acquisition card 21 is configured to acquire an R-angle portion image of the curved glass workpiece 3 acquired by the second area-array camera 18; the photoelectric sensor 10 is arranged at a corresponding position on the transmission guide rail 1 and is used for detecting the current position of the transparent object stage 2; the third computer 22 is connected to the motion control card and motor 4, the third image acquisition card 21, the photoelectric sensor 10 and the rotary table 12 through data lines, and is used for controlling the detection station of the R-angle part and identifying the defects of the R-angle part on line. The second area-array camera 18 is a high-resolution area-array camera, and acquires an image by a telecentric lens 19 in a manner that a parallel light source 20 is used for backlight.
The first computer 11, the second computer 17 and the third computer 22 are all high-performance computers, respectively control the operation of three detection stations, and respectively perform defect-free judgment and defect type judgment on images in the first image acquisition card 9, the second image acquisition card 16 and the third image acquisition card 21, wherein typical defects of the mobile phone curved glass include defects such as pits, scratches, edge breakage, burns and the like.
There are various ways to judge whether there is defect or not and whether there is defect type, such as fast identification of image based on convolutional neural network model.
The working principle is as follows:
the positions and postures of the linear scanning camera 5, the first area-array camera 13 and the second area-array camera 18, the linear coaxial light source 7, the strip-shaped light source 15 and the parallel light source 20 are adjusted in sequence in the three detection stations, so that clear imaging can be ensured in the three detection stations;
placing the curved glass workpiece 3 on a transparent object stage 2 which is sequentially placed on a conveying guide rail 1 at fixed intervals, wherein the transparent object stage 2 and the curved glass workpiece 3 are relatively fixed and kept horizontal;
the first computer 11 sends an instruction to the motion control card and the motor 4, so that the motion control card and the motor 4 drive the transmission guide rail 1 and the transparent object stage 2 to move forwards at a constant speed;
when the transparent object stage 2 moves to the plane detection station, the corresponding object stage position detection device sends a signal to the first computer 11, and the first computer 11 acquires an image from the line scanning camera 5 through the first image acquisition card 9; when the corresponding object stage position detection device detects that the object stage 2 completely passes through, the corresponding object stage position detection device sends a signal to the first computer 11 again and stops image acquisition;
when the transparent object stage 2 moves to the curved edge detection station, the corresponding object stage position detection device sends a signal to the second computer 17, the second computer 17 acquires an image from the first area array camera 13 through the first image acquisition card 9, meanwhile, the second computer 17 sends an instruction to the rotating table 12, after the rotating table 12 rotates 180 degrees with the transparent object stage 2, a signal is fed back to the second computer 17, the signal triggers the second computer 17 to acquire an image again, and then the acquisition is stopped;
when the transparent object stage 2 moves to the R-angle detection station, the corresponding object stage position detection device sends a signal to the third computer 22, the third computer 22 acquires an image from the second area-array camera 18 through the second image acquisition card 1, meanwhile, the third computer 22 sends an instruction to the rotary table 12, after the rotary table 12 rotates 90 degrees with the object stage 2, the signal is fed back to the third computer 22, the signal triggers the third computer 22 to acquire the image again, and the image is rotated 3 times to acquire four R-angle partial images in total;
after the first computer 11, the second computer 17 and the third computer 22 respectively acquire the image data of the line scan camera 5, the first area-array camera 13 and the second area-array camera 18, respectively dividing the received original image into a plurality of sub-images with the same size as the training sample image, rapidly identifying the sub-images based on the corresponding convolutional neural network model, judging whether the original image belongs to has defects or not and judging the types of the defects, thereby completing the on-line detection of typical defects of the mobile phone curved glass, wherein the typical defects of the mobile phone curved glass comprise defects such as pits, scratches, edge breakage, burns and the like, for example, the first computer 11 divides the original image obtained by the line scan camera 5 into a plurality of sub-images, rapidly identifying the subimages based on the convolutional neural network, and judging whether the subimages belong to the original images without defects or the defect types, thereby completing the online defect detection of the curved glass plane part of the mobile phone; similarly, as with the plane detection station, the second computer 17 performs fast identification on the sub-image acquired by the first area-array camera 13 based on the convolutional neural network, and judges whether the original image to which the sub-image belongs has defects and defect types, so as to complete online defect detection of the curved edge part of the curved glass of the mobile phone; similarly, the third computer 22 performs fast recognition on the sub-image of the image acquired by the second area-array camera 18 based on the convolutional neural network, and determines whether the original image to which the sub-image belongs has defects and defect types, so as to complete online defect detection of the R-angle part of the curved glass of the mobile phone;
and after the first computer 11, the second computer 17 and the third computer 22 finish the detection of the curved glass workpiece, continuing to loop the steps S2 and S3 to form the pipeline detection.
Of course, according to the principle of the current convolutional neural network, before the corresponding convolutional neural network model is used to rapidly identify the sub-image, the method further includes the following steps:
establishing a corresponding convolutional neural network model;
and training and learning the convolutional neural network by adopting background, scratch, pockmark, edge breakage and burn image samples to obtain a trained convolutional neural network model.
That is, before the convolutional neural networks in the first computer 11, the second computer 17, and the third computer 22 perform fast recognition on the sub-images and determine whether the original images belong to the sub-images are defect-free or defect-like, the convolutional neural networks need to be trained by using background, scratch, pit, edge breakage, and burn image samples to reduce errors and improve recognition accuracy of the convolutional neural networks, which is also a conventional step of applying the convolutional neural networks at present.
The embodiments of the present invention are not limited by the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be equivalent replacement modes, and all are included in the scope of the present invention. The foregoing description of the specific embodiments of the invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by those skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (5)
1. The utility model provides a typical defect on-line measuring device of cell-phone curved surface glass which characterized in that includes:
the workpiece conveying device is used for conveying curved glass workpieces (3) to be detected and comprises a conveying guide rail (1), a plurality of transparent object stages (2), a motion control card, a motor and a rotating table (12), wherein the conveying guide rail (1) is in driving connection with the motion control card and the motor, the rotating table (12) is in driving connection with the transparent object stages (2), and the transparent object stages (2) are arranged on the conveying guide rail (1) at intervals;
a plane part detection device, a curved edge part detection device and an R angle part detection device which respectively form a plane detection station, a curved edge detection station and an R angle detection station are sequentially arranged along the transmission direction of the workpiece transmission device,
the plane part detection device comprises an object stage position detection device, a first computer (11), a line scanning camera (5) with a lens (6), a linear coaxial light source (7), a light source controller (8) and a first image acquisition card (9), wherein the light source controller (8) is used for adjusting the brightness of the linear coaxial light source (7), and the linear coaxial light source (7) irradiates the plane part of the curved glass workpiece (3); the first image acquisition card (9) is used for acquiring the image of the plane part of the curved glass workpiece (3) acquired by the line scanning camera (5); the objective table position detection device is used for detecting the current position of the transparent objective table (2); the first computer (11) is respectively connected with the first image acquisition card (9), the motion control card and the motor, the rotating platform (12) and the objective table position detection device through data lines and is used for finishing the control of plane part detection and the on-line defect identification of the plane part;
the curved edge part detection device comprises a first area array camera (13) provided with a second lens (14), a strip-shaped light source (15), a light source controller (8), a second image acquisition card (16), a second computer (17) and an object stage position detection device, wherein the light source controller (8) is used for controlling the strip-shaped light source (15), the strip-shaped light source (15) irradiates the curved edge part of the curved glass workpiece (3), and the second image acquisition card (16) is used for acquiring the image of the curved edge part of the curved glass workpiece (3) acquired by the first area array camera (13); the objective table position detection device is used for detecting the current position of the transparent objective table (2); the second computer (17) is respectively connected with the motor motion control card, the second image acquisition card (16), the object stage position detection device and the rotating table (12) through data lines and is used for finishing the control of the detection of the curved edge part and the online defect identification of the curved edge part;
the R angle part detection device comprises a third computer (22), an object stage position detection device, a second area array camera (18) provided with a telecentric lens (19), a parallel light source (20), a light source controller (8) and a third image acquisition card (21), wherein the light source controller (8) controls the brightness of the parallel light source (20), and the third image acquisition card (21) is used for acquiring an R angle part image of the curved glass workpiece (3) acquired by the second area array camera (18); the objective table position detection device is used for detecting the current position of the transparent objective table (2); and the third computer (22) is respectively connected with the motion control card and the motor, the third image acquisition card (21), the objective table position detection device and the rotating table (12) through data lines and is used for finishing the control of the detection station of the R angle part and the online defect identification of the R angle part.
2. The device for on-line detection of typical defects of curved glass of mobile phone according to claim 1, wherein the line scan camera (5) is vertically opposite to the plane part of the curved glass workpiece (3), the line scan field is larger than the width of the curved glass sample, the light emitted by the linear coaxial light source (7) forms an included angle with the line scan camera (5), and the light bars have uniform brightness and can cover the curved glass workpiece (3).
3. The device for on-line detection of typical defects of curved glass of mobile phone according to claim 1, wherein the first area-array camera (13) is a high-resolution CMOS area-array camera, the axis of the camera passes through the curvature center of the curved edge of the curved glass workpiece (3) and forms a certain included angle α with the horizontal direction, the two strip-shaped light sources (15) are symmetrically arranged at two sides of the first area-array camera (13) and the lighting angle β ensures that the strip-shaped light sources fall into the range of the target surface of the first area-array camera (13) after being reflected by the curved edge to form a clear image.
4. The device for detecting the typical defects of the curved glass of the mobile phone according to claim 1, wherein the parallel light source (20) and the second area array camera (18) vertically face the R-angle part of the curved glass workpiece (3), the second area array camera (18) is a high-resolution area array camera, and images are acquired through a telecentric lens (19) in a mode that the parallel light source (20) is used for backlighting.
5. The device for detecting the typical defects of the curved glass of the mobile phone according to claim 1, wherein the stage position detection device adopts a photoelectric sensor (10) and is respectively installed at corresponding positions on the conveying guide rail (1).
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110018178A (en) * | 2019-04-28 | 2019-07-16 | 华南理工大学 | A kind of mobile phone bend glass typical defect on-line measuring device and method |
| CN111999316A (en) * | 2020-09-02 | 2020-11-27 | 惠州高视科技有限公司 | Curved glass detection system and method |
| CN112014414A (en) * | 2020-08-14 | 2020-12-01 | 西安电子科技大学 | System and method for detecting defects of mobile phone glass cover plate |
| CN113834445A (en) * | 2021-10-04 | 2021-12-24 | 东北大学 | Method for detecting sizes of slag and burr in casting blank flame cutting |
| CN114166854A (en) * | 2021-12-09 | 2022-03-11 | 苏州华星光电技术有限公司 | Curved surface screen defect detection method and detection device |
| CN114486939A (en) * | 2022-04-08 | 2022-05-13 | 欧普康视科技股份有限公司 | Lens scratch detection system and method |
| CN114486929A (en) * | 2022-01-20 | 2022-05-13 | 深圳佳视德智能科技有限公司 | Method and device for detecting appearance of automobile door trim |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110018178A (en) * | 2019-04-28 | 2019-07-16 | 华南理工大学 | A kind of mobile phone bend glass typical defect on-line measuring device and method |
| CN112014414A (en) * | 2020-08-14 | 2020-12-01 | 西安电子科技大学 | System and method for detecting defects of mobile phone glass cover plate |
| CN111999316A (en) * | 2020-09-02 | 2020-11-27 | 惠州高视科技有限公司 | Curved glass detection system and method |
| CN113834445A (en) * | 2021-10-04 | 2021-12-24 | 东北大学 | Method for detecting sizes of slag and burr in casting blank flame cutting |
| CN114166854A (en) * | 2021-12-09 | 2022-03-11 | 苏州华星光电技术有限公司 | Curved surface screen defect detection method and detection device |
| CN114486929A (en) * | 2022-01-20 | 2022-05-13 | 深圳佳视德智能科技有限公司 | Method and device for detecting appearance of automobile door trim |
| CN114486929B (en) * | 2022-01-20 | 2024-02-02 | 深圳佳视德智能科技有限公司 | Automobile door trim appearance detection method and device |
| CN114486939A (en) * | 2022-04-08 | 2022-05-13 | 欧普康视科技股份有限公司 | Lens scratch detection system and method |
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