CN115661159A - Panel defect enhancement detection method, system, device and medium - Google Patents
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Abstract
The invention discloses a method, a system, a device and a medium for enhancing and detecting panel defects, which relate to the technical field of display panel detection, and are characterized in that a gray curve corresponding to a panel to be detected is matched with a gray curve of a sample panel by obtaining the sample panel and a corresponding first gray enhancement parameter, and the corresponding gray enhancement parameter is selected according to a matching result, so that the automatic enhancement of an image of the panel to be detected is realized; the image is enhanced based on spline regression, the image details are retained, and the defect detection accuracy is improved; the panel monochrome image is processed, the panel dead pixel is positioned, and the automatic enhancement and the defect detection of the panel image are realized.
Description
Technical Field
The invention relates to the technical field of display panel detection, in particular to a method, a system, a device and a medium for enhancing and detecting panel defects.
Background
With the rapid development of the panel industry, the types of panels are more and more diversified, the panel manufacturing process is more and more complicated and finer, and the requirements of the market on the quality of the panels are improved. The factors influencing the quality of the panel are many, and the defects of different shapes of the panel can be caused by process fluctuation and machine table difference in the production process. mura is a common defect in the panel manufacturing process, and the term mura is derived from japanese transliteration and means "spots", i.e., defects in which the panel portion shows uneven brightness. mura defects can reduce panel performance and affect user experience, and therefore, strengthening the detection of the mura defects of the panel is very important. However, the contrast of the area where the mura is located and the surrounding background is low, the edge is fuzzy, the shape of the mura is not fixed, the current AOI (Automated Optical Inspection) technology cannot accurately detect the mura defect, the existing panel production enterprises generally need to manually perform visual Inspection on the panel to confirm the condition of the panel defect, a large amount of time cost and labor cost are consumed, and therefore, the problem of how to automatically and accurately detect the mura defect of the panel needs to be solved urgently.
Disclosure of Invention
In order to automatically and accurately detect the mura defect of the panel, improve the working efficiency, reduce the labor cost and improve the quality of panel products, the invention provides a panel defect enhancement detection method, which comprises the following steps:
step 1: collecting a sample panel image and obtaining a corresponding first gray curve;
step 2: carrying out gray level enhancement on the sample panel image, and recording a corresponding first gray level enhancement parameter;
and step 3: acquiring a gray image of a panel to be detected to obtain a corresponding second gray curve;
and 4, step 4: matching the first gray scale curve and the second gray scale curve to obtain a matching result, and obtaining a corresponding second gray scale enhancement parameter according to the matching result and the first gray scale enhancement parameter;
and 5: carrying out gray level enhancement on the gray level image according to the second gray level enhancement parameter to obtain a target panel image;
and 6: and analyzing the target panel image to obtain first panel defect data.
The method comprises the following steps: acquiring a sample panel image and obtaining a corresponding first gray scale curve, carrying out gray scale enhancement on the sample panel image, and recording a first gray scale enhancement parameter when a gray scale enhancement parameter is adjusted to be capable of clearly distinguishing details of the sample panel image, wherein the first gray scale curve corresponds to the first gray scale enhancement parameter; acquiring a gray image of a panel to be detected and obtaining a corresponding second gray curve, and then matching the first gray curve with the second gray curve, wherein if the first gray curve is matched with the second gray curve, the corresponding gray image is considered to be similar to the corresponding sample panel image, so that the same gray enhancement parameters can be applied; and selecting a corresponding first gray level enhancement parameter as a second gray level enhancement parameter of the gray level image according to a matching result, enhancing the gray level image according to the second gray level enhancement parameter, so that mura defects in the image can be clearly seen, finally identifying the enhanced gray level image, accurately finding the mura defects in the panel image, realizing automatic enhancement and defect detection of the panel image, and improving the working efficiency.
Further, since the gray level of the panel image is uniformly distributed, the gray level change operation performed on the panel image may cause the loss of details in the panel image, and therefore, in order to ensure the image enhancement effect and avoid the loss of image details, it is necessary to first fit the gray level distribution data of the image, obtain the gray level enhancement parameters according to the fitted curve, and then apply the gray level enhancement parameters to the image before gray level fitting, and therefore, step 2 specifically includes:
obtaining image gray distribution data corresponding to the sample panel image;
fitting the image gray level distribution data to obtain a fitting curve;
and enhancing the image gray distribution data according to the fitting curve, and recording corresponding first gray enhancement parameters.
Because the corresponding gray scale interval of the mura area in the gray scale image is relatively fixed, in order to further ensure the integrity of the details of the mura area in the image, the image gray scale distribution data needs to be subjected to piecewise fitting, and the details of the mura area in the panel image are further reserved; the spline regression can determine an anchor point as required, and then segment fitting is carried out on data according to the known anchor point, so that after image gray scale distribution data corresponding to a sample panel image is obtained, at least one anchor point is obtained according to the image gray scale distribution data, and then fitting is carried out on the image gray scale distribution data according to a spline regression algorithm and the anchor point, wherein the anchor point is used for segmenting the image gray scale distribution data.
Further, in the panel manufacturing process, there is a difference between panel images generated by different processes of different machines, and in order to perform targeted image enhancement on the panel images corresponding to different processes or machines and ensure the effectiveness of image enhancement, image enhancement parameters need to be adjusted according to the types of the panel processes and the machines, so step 1 specifically includes: acquiring a sample panel image and acquiring a corresponding first gray curve and first data, wherein the first data comprises process data and equipment data;
the step 3 specifically comprises the following steps: acquiring a gray level image of a panel to be detected, and acquiring a corresponding second gray level curve and second data, wherein the second data comprises process data and equipment data;
the step 4 specifically comprises the following steps: matching the first gray scale curve and the second gray scale curve to obtain a first matching result; matching the first data and the second data to obtain a second matching result; when the process data, the equipment data and the gray defect corresponding to a certain gray image correspond to a certain sample panel image, the first gray enhancement parameter corresponding to the sample panel image can be used as the second gray enhancement parameter of the gray image; therefore, a corresponding second gray scale enhancement parameter is obtained according to the first matching result, the second matching result and the first gray scale enhancement parameter;
the step 5 specifically comprises the following steps: and carrying out gray level enhancement on the gray level image according to the second gray level enhancement parameter to obtain a target panel image.
Further, in order to improve the detection efficiency, the system scales the grayscale image according to the resolution of the grayscale image, and matches the grayscale image by detecting the grayscale distribution of the thumbnail, which can be completed with a small amount of calculation, so step 3 is specifically: acquiring a gray image of the panel to be detected to obtain the resolution of the gray image;
zooming the gray level image according to the resolution ratio to obtain a corresponding thumbnail;
obtaining a third gray curve corresponding to the thumbnail;
the step 4 specifically comprises the following steps: and matching the third gray curve with the first gray curve to obtain a matching result, and obtaining a corresponding second gray enhancement parameter according to the matching result and the first gray enhancement parameter.
Further, since the panel grayscale image generally includes a panel portion and a background portion, the grayscale distribution of the background portion is complex, but cannot be used as a basis for enhancing the panel image; the panel part comprises a normal part and a defect part, and the gray distribution of the normal part is uniform, so that the integral gray distribution characteristic of the panel is not obvious; the gray distribution of the defect part has difference and can be used as the basis for enhancing the panel image; therefore, effective and obvious gray characteristic can be obtained by screening the panel gray image in a partition manner, the matching precision of the panel image is optimized, and the image enhancement effect is improved, wherein the step 3 specifically comprises the following steps:
collecting a gray image of a panel to be detected, and partitioning the gray image to obtain at least two sub-regions;
numbering the sub-regions, and respectively obtaining fourth gray curves corresponding to the sub-regions;
screening the fourth gray curve to obtain a screening result;
the step 4 specifically comprises the following steps: matching the fourth gray curve with the first gray curve to obtain a matching result; and obtaining a corresponding second gray scale enhancement parameter according to the matching result and the first gray scale enhancement parameter.
Further, common panel defects include panel dead spots in addition to mura, and the panel dead spots may occur when a screen displays one or more of single colors of red, green, blue, and the like, so that detection of the panel dead spots requires first performing differential processing on a single panel image and then identifying an obtained differential image; therefore, the method for obtaining the defect data of the first panel further comprises the following steps:
collecting a monochrome image of the panel to be detected;
carrying out difference processing on the monochrome image to obtain a difference image;
and analyzing the differential image to obtain the defect data of the second panel.
Wherein mura is a panel brightness defect, dead spots are a panel color defect, and the detection of the dead spots is possibly influenced due to a large mura range; in order to avoid mura from influencing the detection of dead pixels of the panel and improve the accuracy of detecting panel defects, after the monochrome image is obtained, the monochrome image is compensated according to the first panel defect data, and then the monochrome image is subjected to difference processing to obtain the difference image.
To achieve the above object, the present invention also provides a panel defect enhancement detection system, comprising:
the image acquisition unit is used for acquiring a sample panel image to obtain a corresponding first gray curve; collecting a gray level image of the panel to be detected to obtain a corresponding second gray level curve;
the image processing unit is used for carrying out gray level enhancement on the sample panel image and recording a corresponding first gray level enhancement parameter;
the image matching unit is used for matching the first gray scale curve and the second gray scale curve and obtaining a corresponding second gray scale enhancement parameter according to a matching result and the first gray scale enhancement parameter;
the defect detection unit is used for carrying out gray level enhancement on the gray level image according to the second gray level enhancement parameter to obtain a target panel image; and analyzing the target panel image to obtain first panel defect data.
Wherein, the principle of the system is as follows: the image acquisition unit acquires a sample panel image and a gray level image of a panel to be detected, and respectively acquires a corresponding first gray level curve and a corresponding second gray level curve; the image processing unit performs gray level enhancement on the sample panel image and records a corresponding first gray level enhancement parameter; the image matching unit matches the first gray curve with the second gray curve, if the first gray curve is matched with the second gray curve, the corresponding gray image is considered to be similar to the corresponding sample panel image, the same gray enhancement parameters can be applied, the corresponding first gray enhancement parameters are selected as the second gray enhancement parameters of the gray image according to the matching result, finally, the defect detection unit performs gray enhancement on the gray image according to the second gray enhancement parameters, so that mura defects in the image are clearly visible, finally, the enhanced image is analyzed, mura defects in the panel image are found, automatic selection of the image gray enhancement parameters in the panel defect enhancement detection process is achieved, the mura defects are automatically detected, and the working efficiency is improved.
In order to achieve the above object, the present invention further provides a panel defect enhancement detection apparatus, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the panel defect enhancement detection methods when executing the computer program.
To achieve the above object, the present invention further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of any one of the above panel defect enhancement detection methods.
One or more technical schemes provided by the invention at least have the following technical effects or advantages: matching the gray curve corresponding to the panel to be detected with the gray curve of the sample panel by obtaining the sample panel and the corresponding first gray enhancement parameter, and selecting the corresponding gray enhancement parameter according to the matching result, thereby realizing the automatic enhancement of the image of the panel to be detected; the image is enhanced based on spline regression, the image details are retained, and the defect detection accuracy is improved; the monochrome image of the panel is processed, the dead pixel of the panel is positioned, and the automatic enhancement and the defect detection of the panel image are realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention;
FIG. 1 is a schematic view of a panel defect enhancement inspection process according to the present invention;
FIG. 2 is a schematic diagram of a system for enhanced detection of panel defects according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other without conflicting with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described and thus the scope of the present invention is not limited by the specific embodiments disclosed below.
Example one
Referring to fig. 1, an embodiment of the present invention provides a method for detecting a defect enhancement of a panel, including the following steps:
step 1: collecting a sample panel image and obtaining a corresponding first gray curve;
step 2: carrying out gray level enhancement on the sample panel image, and recording a corresponding first gray level enhancement parameter;
and 3, step 3: acquiring a gray image of a panel to be detected to obtain a corresponding second gray curve;
and 4, step 4: matching the first gray scale curve and the second gray scale curve to obtain a matching result, and obtaining a corresponding second gray scale enhancement parameter according to the matching result and the first gray scale enhancement parameter;
and 5: carrying out gray level enhancement on the gray level image according to the second gray level enhancement parameter to obtain a target panel image;
and 6: and analyzing the target panel image to obtain first panel defect data.
The method for performing gray level enhancement on the sample panel image may be a histogram equalization algorithm, a histogram stipulation algorithm, an adaptive histogram equalization algorithm, or an adaptive histogram equalization algorithm that limits contrast, and the specific method is determined according to actual needs, and the embodiment is not specifically limited herein; the method for performing gray scale enhancement on the gray scale image should be a gray scale enhancement method consistent with that for the sample panel image, and this embodiment is not particularly limited herein.
The matching of the first gray scale curve and the second gray scale curve can be realized through a machine learning model based on a random forest algorithm, a neural network algorithm or a K-nearest neighbor algorithm, specifically, a machine learning model is firstly established, the first gray scale curve is used as a training sample and is input into the machine learning model to obtain a matching model, the second gray scale curve is input into the prediction model, and finally a matching result is obtained, the specific algorithm selected by the machine learning model is determined according to actual needs, and the embodiment is not specifically limited herein.
The target panel image is an enhanced image, the analyzing of the target panel image is to perform contour detection on the target panel image, and if an interested area in the target panel image has a contour, the panel is considered to have a mura defect, wherein the interested area is determined according to actual needs, which is not specifically limited in this embodiment.
The contour detection may be implemented by a Canny operator or a LoG edge detection algorithm, and the specific method is determined according to actual needs, which is not specifically limited in this embodiment.
In this embodiment, the step 2 specifically includes: obtaining image gray distribution data corresponding to the sample panel image;
fitting the image gray distribution data to obtain a fitting curve;
and enhancing the image gray level distribution data according to the fitting curve, and recording corresponding first gray level enhancement parameters.
In this embodiment, after obtaining image gray distribution data corresponding to a sample panel image, at least one anchor point is obtained according to the image gray distribution data, and then the image gray distribution data is fitted according to a spline regression algorithm and the anchor point, where the anchor point is used to segment the image gray distribution data.
The gray distribution of the mura area in the panel image is within a range of 120 to 160, so that the number of the anchor points is preferably two, the gray distribution data is divided into three sections of 0 to 120, 120 to 160 and 120 to 225, the number of the anchor points and the positions of the anchor points can be adjusted according to actual needs, and the embodiment is not particularly limited herein.
Specifically, after obtaining the image before the panel defect enhancement, fitting the image gray distribution data, obtaining a first gray enhancement parameter according to a fitting curve, and applying the first gray enhancement parameter to the image before the gray fitting to obtain the image after the panel defect enhancement.
In the panel manufacturing process, the panel images generated by different processes of different machines have differences, and in order to perform targeted image enhancement on the panel images corresponding to different processes or machines and ensure the effectiveness of the image enhancement, image enhancement parameters need to be adjusted according to the panel processes and the machine types, so in this embodiment, step 1 specifically is: acquiring a sample panel image and acquiring a corresponding first gray curve and first data, wherein the first data comprises process data and equipment data;
the step 3 specifically comprises the following steps: acquiring a gray image of a panel to be detected to obtain a corresponding second gray curve and second data, wherein the second data comprises process data and equipment data;
the step 4 specifically comprises the following steps: matching the first gray scale curve and the second gray scale curve to obtain a first matching result; matching the first data and the second data to obtain a second matching result; obtaining a corresponding second gray scale enhancement parameter according to the first matching result, the second matching result and the first gray scale enhancement parameter;
the step 5 specifically comprises the following steps: and carrying out gray level enhancement on the gray level image according to the second gray level enhancement parameter to obtain a target panel image.
The matching rule of the first data and the second data is determined according to actual needs, and this embodiment is not specifically limited herein.
Example two
Referring to fig. 1, a second embodiment of the present invention provides a method for detecting a defect enhancement of a panel, where on the basis of the first embodiment, the step 3 specifically includes: acquiring a gray image of the panel to be detected to obtain the resolution of the gray image;
zooming the gray level image according to the resolution ratio to obtain a corresponding thumbnail;
obtaining a third gray curve corresponding to the thumbnail;
the step 4 specifically comprises the following steps: and matching the third gray curve with the first gray curve to obtain a matching result, and obtaining a corresponding second gray enhancement parameter according to the matching result and the first gray enhancement parameter.
Specifically, the machine learning model is firstly established, the first gray curve is used as a training sample and is input into the machine learning model to obtain a matching model, the second gray curve is input into the prediction model, and finally a matching result is obtained.
The scaling of the grayscale image is determined according to the resolution of the actually obtained image, which is not specifically limited in this embodiment.
EXAMPLE III
Referring to fig. 1, a third embodiment of the present invention provides a method for detecting a defect enhancement of a panel, where on the basis of the first embodiment, the step 3 specifically includes: collecting a gray image of a panel to be detected, and partitioning the gray image to obtain at least two sub-areas;
numbering the sub-regions, and respectively obtaining fourth gray curves corresponding to the sub-regions;
screening the fourth gray curve to obtain a screening result;
the step 4 specifically comprises the following steps: and matching the fourth gray curve with the first gray curve to obtain a matching result, and obtaining a corresponding second gray enhancement parameter according to the matching result and the first gray enhancement parameter.
The number and size of the sub-regions are determined according to actual needs, and the embodiment is not specifically limited herein; because the panel gray image generally comprises a panel part and a background part, the gray distribution of the background part is complex and cannot be used as the basis for enhancing the panel image; the panel part comprises a normal part and a defect part, and the gray distribution of the normal part is uniform, so that the integral gray distribution characteristic of the panel is not obvious; the gray distribution of the defect portion has a difference and can be used as a basis for enhancing the panel image, therefore, the fourth gray curve includes the fourth gray curve corresponding to the background portion and the normal portion, which is filtered, i.e., deleted, and the specific filtering rule is determined according to actual needs, which is not specifically limited in this embodiment.
In this embodiment, the method for obtaining the defect data of the first panel further includes the following steps:
collecting a monochrome image of the panel to be detected;
carrying out difference processing on the monochrome image to obtain a difference image;
and analyzing the differential image to obtain the defect data of the second panel.
The monochrome image may be any one or more monochrome images of red, blue, green, and the like, and specific colors and numbers are determined according to actual needs, which is not specifically limited in this embodiment.
The differential image may be obtained by subtracting the monochromatic image, or may be obtained by subtracting the monochromatic image from a standard image, and the specific processing manner is determined according to actual needs, which is not specifically limited in this embodiment.
Specifically, the differential image is analyzed, that is, the number of the pixel points in the differential image is calculated, whether a dead pixel exists in the panel to be detected can be determined according to the number of the pixel points, a specific determination standard is determined according to actual needs, and the embodiment is not specifically limited herein.
In the present embodiment, after obtaining the monochrome image, the monochrome image is first compensated according to the first panel defect data, and then the difference image is obtained by performing the difference processing on the monochrome image.
Example four
Referring to fig. 2, a fourth embodiment of the present invention provides a system for detecting defects of a panel, including:
the image acquisition unit is used for acquiring a sample panel image to obtain a corresponding first gray curve; acquiring a gray image of a panel to be detected to obtain a corresponding second gray curve;
the image processing unit is used for carrying out gray level enhancement on the sample panel image and recording a corresponding first gray level enhancement parameter;
the image matching unit is used for matching the first gray scale curve and the second gray scale curve and obtaining a corresponding second gray scale enhancement parameter according to a matching result and the first gray scale enhancement parameter;
the defect detection unit is used for carrying out gray level enhancement on the gray level image according to the second gray level enhancement parameter to obtain a target panel image; and analyzing the target panel image to obtain first panel defect data.
EXAMPLE five
The fifth embodiment of the present invention provides a panel defect enhancement detection apparatus, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the steps of the panel defect enhancement detection method when executing the computer program.
EXAMPLE six
An embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the panel defect enhancement detection method are implemented.
The processor may be a Central Processing Unit (CPU), other general purpose processor, a digital signal processor (digital signal processor), an Application Specific Integrated Circuit (Application Specific Integrated Circuit), an off-the-shelf programmable gate array (Field programmable gate array) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or any conventional processor.
The memory may be used for storing the computer program and/or the module, and the processor may implement various functions of the panel defect enhancement detection apparatus in the invention by operating or executing data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage device.
The panel defect enhancement detection apparatus, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, all or part of the flow of the method of the embodiments described above can be realized by the present invention, and the computer program can be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments described above can be realized. Wherein the computer program comprises computer program code, an object code form, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying said computer program code, a recording medium, a usb-disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, a point carrier signal, a telecommunications signal, a software distribution medium, etc. It should be noted that the computer readable medium may contain content that is appropriately increased or decreased as required by legislation and patent practice in the jurisdiction.
Having described the basic concept of the invention, it should be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely as illustrative and not restrictive of the broad invention. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such alterations, modifications, and improvements are intended to be suggested in this specification, and are intended to be within the spirit and scope of the exemplary embodiments of this specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C + +, C #, VB.NET, python, and the like, a conventional programming language such as C, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service using, for example, software as a service (SaaS).
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single disclosed embodiment.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document is inconsistent or contrary to the present specification, and except where the application history document is inconsistent or contrary to the present specification, the application history document is not inconsistent or contrary to the present specification, but is to be read in the broadest scope of the present claims (either currently or hereafter added to the present specification). It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of the present specification shall control if they are inconsistent or inconsistent with the statements and/or uses of the present specification. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (11)
1. A panel defect enhancement detection method is characterized by comprising the following steps:
step 1: collecting a sample panel image and obtaining a corresponding first gray curve;
step 2: carrying out gray level enhancement on the sample panel image, and recording a corresponding first gray level enhancement parameter;
and step 3: acquiring a gray image of a panel to be detected to obtain a corresponding second gray curve;
and 4, step 4: matching the first gray scale curve and the second gray scale curve to obtain a matching result, and obtaining a corresponding second gray scale enhancement parameter according to the matching result and the first gray scale enhancement parameter;
and 5: carrying out gray level enhancement on the gray level image according to the second gray level enhancement parameter to obtain a target panel image;
step 6: and analyzing the target panel image to obtain first panel defect data.
2. The method for detecting the defect of the panel as claimed in claim 1, wherein the step 2 is specifically as follows: obtaining image gray distribution data corresponding to the sample panel image;
fitting the image gray distribution data to obtain a fitting curve;
and enhancing the image gray distribution data according to the fitting curve, and recording corresponding first gray enhancement parameters.
3. The method of claim 2, wherein after obtaining image gray distribution data corresponding to a sample panel image, at least one anchor point is obtained according to the image distribution data, and then the image gray distribution data is fitted according to a spline regression algorithm and the anchor point, wherein the anchor point is used for segmenting the image distribution data.
4. The method for detecting the panel defect enhancement as claimed in claim 1, wherein the step 1 is specifically as follows: acquiring a sample panel image and acquiring a corresponding first gray curve and first data, wherein the first data comprises process data and equipment data;
the step 3 specifically comprises the following steps: acquiring a gray level image of a panel to be detected, and acquiring a corresponding second gray level curve and second data, wherein the second data comprises process data and equipment data;
the step 4 specifically comprises the following steps: matching the first gray scale curve and the second gray scale curve to obtain a first matching result; matching the first data and the second data to obtain a second matching result; obtaining a corresponding second gray scale enhancement parameter according to the first matching result, the second matching result and the first gray scale enhancement parameter;
the step 5 specifically comprises the following steps: and carrying out gray level enhancement on the gray level image according to the second gray level enhancement parameter to obtain a target panel image.
5. The method for detecting the defect of the panel as claimed in claim 1, wherein the step 3 is specifically as follows: acquiring a gray image of the panel to be detected to obtain the resolution of the gray image;
zooming the gray level image according to the resolution ratio to obtain a corresponding thumbnail;
obtaining a third gray curve corresponding to the thumbnail;
the step 4 specifically comprises the following steps: and matching the third gray curve with the first gray curve to obtain a matching result, and obtaining a corresponding second gray enhancement parameter according to the matching result and the first gray enhancement parameter.
6. The method for detecting the defect of the panel as claimed in claim 1, wherein the step 3 is specifically as follows: collecting a gray image of a panel to be detected, and partitioning the gray image to obtain at least two sub-areas;
numbering the sub-regions, and respectively obtaining fourth gray curves corresponding to the sub-regions;
screening the fourth gray curve to obtain a screening result;
the step 4 specifically comprises the following steps: and matching the fourth gray curve with the first gray curve to obtain a matching result, and obtaining a corresponding second gray enhancement parameter according to the matching result and the first gray enhancement parameter.
7. The method of claim 1, wherein obtaining the first panel defect data further comprises:
collecting a monochrome image of the panel to be detected;
carrying out difference processing on the monochrome image to obtain a difference image;
and analyzing the differential image to obtain second panel defect data.
8. The method according to claim 7, wherein the monochrome image is obtained, and then the difference image is obtained by first compensating the monochrome image according to the first panel defect data and then performing difference processing on the monochrome image.
9. A panel defect enhancement detection system, the system comprising:
the image acquisition unit is used for acquiring a sample panel image to obtain a corresponding first gray curve; collecting a gray level image of the panel to be detected to obtain a corresponding second gray level curve;
the image processing unit is used for carrying out gray level enhancement on the sample panel image and recording a corresponding first gray level enhancement parameter;
the image matching unit is used for matching the first gray scale curve and the second gray scale curve and obtaining a corresponding second gray scale enhancement parameter according to a matching result and the first gray scale enhancement parameter;
the defect detection unit is used for carrying out gray level enhancement on the gray level image according to the second gray level enhancement parameter to obtain a target panel image; and analyzing the target panel image to obtain first panel defect data.
10. A panel defect enhancement detection apparatus comprising a memory, a processor and a computer program stored in said memory and executable on said processor, wherein said processor implements the steps of the panel defect enhancement detection method according to any one of claims 1 to 8 when executing said computer program.
11. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the panel defect enhanced detection method according to any one of claims 1 to 8.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211702022.3A CN115661159B (en) | 2022-12-29 | 2022-12-29 | Panel defect enhancement detection method, system, device and medium |
| PCT/CN2023/085359 WO2024138933A1 (en) | 2022-12-29 | 2023-03-31 | Panel defect enhancement detection method, system and apparatus, and medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211702022.3A CN115661159B (en) | 2022-12-29 | 2022-12-29 | Panel defect enhancement detection method, system, device and medium |
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| WO2024138933A1 (en) * | 2022-12-29 | 2024-07-04 | 成都数联云算科技有限公司 | Panel defect enhancement detection method, system and apparatus, and medium |
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| WO2024138933A1 (en) | 2024-07-04 |
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