CN113808136A - Liquid crystal screen defect detection method and system based on nearest neighbor algorithm - Google Patents
Liquid crystal screen defect detection method and system based on nearest neighbor algorithm Download PDFInfo
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Abstract
The invention provides a liquid crystal screen defect detection method and system based on a nearest neighbor algorithm. The method comprises the following steps: calculating corner information of the image according to the acquired image of the known defect-free liquid crystal screen; calculating the statistical characteristic of the position of the non-defect corner according to the corner information; and calculating the defect position of the screen to be detected by utilizing a nearest neighbor algorithm according to the statistical characteristics of the positions of the non-defect angular points. And completing the defect detection of the liquid crystal screen. The invention is based on the visual image processing technology, realizes the defect detection of the liquid crystal screen through the nearest neighbor algorithm, and has high precision, high speed and good robustness.
Description
Technical Field
The invention relates to the technical field of computer vision detection, in particular to a liquid crystal screen defect detection method and system based on a nearest neighbor algorithm.
Background
The computer vision technology has the advantages of non-contact property, economy, flexibility, integration and the like, and has wide application prospect in the field of industrial testing and online detection. The defect detection of the liquid crystal panel in the current liquid crystal panel industry is one of the indispensable key steps of quality control.
The existing solution most similar to the present invention is a visual inspection method and device for defects of liquid crystal screens, which performs defect inspection by calculating the gray value matching degree of a template image and an image to be inspected. For example, chinese patent application No. CN201410546795.6 relates to a method and apparatus for visually inspecting defects on a liquid crystal display, which adjusts the button positions of a matrix electromagnetic switch button panel according to the key arrangement of a sample to be inspected, so that the matrix electromagnetic switch button panel is matched with the key arrangement of the sample to be inspected; acquiring template images corresponding to the buttons in an off-line manner, and numbering and storing the template images; controlling the matrix electromagnetic switch button panel button to press a corresponding key of the sample to be tested according to the various combined working states; collecting a liquid crystal screen image corresponding to each key of a sample to be detected when the key is pressed in real time, and respectively selecting a frame of image for preprocessing; and carrying out template matching calculation on the preprocessed image and the template image to obtain a defect visual detection result.
The main disadvantages of the prior art are complex hardware system design, low precision, high requirement for the difference of different screen processes, high cost and the like.
Disclosure of Invention
In order to solve the problems, aiming at the defects of complex design and low precision of a hardware system, the invention realizes the detection of the defects of the liquid crystal screen by taking pictures of the liquid crystal screen, utilizing a visual image processing technology, calculating the statistical characteristics of non-defect areas and utilizing the thought of a nearest neighbor algorithm.
Specifically, the invention provides a liquid crystal screen defect detection method based on a nearest neighbor algorithm, which comprises the following steps:
calculating corner information of the image according to the acquired image of the known defect-free liquid crystal screen;
calculating the statistical characteristic of the position of the non-defect corner according to the corner information;
and calculating the defect position of the screen to be detected by utilizing a nearest neighbor algorithm according to the statistical characteristics of the positions of the non-defect angular points.
Based on the above purpose, the present invention further provides a system for detecting defects of a liquid crystal screen based on a nearest neighbor algorithm, comprising:
the corner point calculation module is used for calculating corner point information of the image according to the collected image of the known defect-free liquid crystal screen;
the non-defect statistical characteristic module is used for calculating the statistical characteristic of the position of a non-defect corner according to the corner information;
and the defect position calculation module is used for calculating the defect position of the screen to be detected by utilizing a nearest neighbor algorithm according to the statistical characteristics of the positions of the non-defect angular points.
The defect detection method based on the nearest neighbor idea of the statistical characteristics is realized by taking pictures of the liquid crystal screen, searching the nearest distance from the statistical characteristics of the angular points to the characteristic table by utilizing a visual image processing technology. Has great application value in the liquid crystal detection industry. High precision, high speed and good robustness.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 shows a flowchart of a liquid crystal screen defect detection method based on a nearest neighbor algorithm according to an embodiment of the present invention.
FIG. 2 is a diagram of an implementation of the statistical property table construction of the present invention;
FIG. 3 is a diagram of exemplary effects of the present invention;
fig. 4 is a configuration diagram showing a liquid crystal screen defect detection system based on a nearest neighbor algorithm according to an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a storage medium according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a flowchart of a liquid crystal screen defect detection method based on a nearest neighbor algorithm according to an embodiment of the present invention. The method comprises the following steps:
1. calculating corner information of an image according to the collected known defect-free liquid crystal screen image, wherein the corner calculating method comprises the following steps: the method comprises a Harris corner algorithm, a FAST corner detection algorithm, a SIFT corner detection algorithm and an SURF corner detection algorithm.
2. For non-defective corner points, digging out n multiplied by n corner point region images by taking the corner points as centers, and calculating the statistical characteristics of the corner points, wherein the statistical characteristics comprise:
the average gray value of the image of the corner region is calculated according to the following formula:
wherein i, j represents the horizontal and vertical coordinates of the angular point region image, n is the width and height of the angular point region image, and f (i, j) represents the gray value of the angular point region image at the coordinates (i, j).
The mean square error of the image of the corner region is calculated according to the following formula:
the gray value summation of the image after the first-order partial derivative along the X direction of the angular point region image, wherein the calculation formula of the first derivative along the X direction is as follows:
wherein x, y represents the horizontal and vertical coordinates of the corner region image, and f (x, y) represents the gray value of the corner region image at the coordinates (x, y).
The gray value summation of the image after the first-order partial derivative along the Y direction of the angular point region image, wherein the calculation formula of the first derivative along the Y direction is as follows:
the sum of gray values after the second derivative of the image in the corner region is calculated by a laplacian operator, wherein the laplacian operator formula is as follows:
wherein:
calculating 5 statistical characteristics of each non-defect corner point, normalizing, and recording to form a statistical characteristic table, as shown in fig. 2, calculating the statistical characteristics of each small block and normalizing, wherein the real defect corner points of D1, D2, D3, and D4 do not participate in the calculation of the statistical characteristics. Wherein the normalization formula is as follows:
where r (x) is the calculated statistical property, min (r) represents the minimum of the 5 properties, max (r) represents the maximum of the 5 properties, and norm (x) is the normalized value.
3. Calculating the defect position of a new screen to be detected by utilizing a neighbor algorithm, wherein the defect position comprises the following steps:
the distance calculation formula of the neighbor algorithm adopts an Euclidean distance, and the formula is as follows:
wherein
A vector consisting of 5 statistical property values representing the corner points calculated on the screen to be inspected,
a vector of feature values calculated for non-defective corner points calculated for previous screens. While
And
respectively represent
Each value in the vector.
After 5 statistical characteristic values of the corner points of the screen to be detected are calculated, a non-defect corner point area closest to the corner point is found out according to the Euclidean distance calculation formula, and if the distance is smaller than or equal to a threshold value T, the corner point is considered to be a normal corner point and not a defect corner point. And if the distance is greater than the threshold value T, the corner point is considered as a defect corner point.
The defect detection method based on the nearest neighbor idea of the statistical characteristics is realized by taking pictures of the liquid crystal screen, searching the nearest distance from the statistical characteristics of the angular points to the characteristic table by utilizing a visual image processing technology. Has great application value in the liquid crystal detection industry.
The application embodiment provides a nearest neighbor algorithm-based liquid crystal screen defect detection system, which is used for executing the nearest neighbor algorithm-based liquid crystal screen defect detection method described in the above embodiment, as shown in fig. 4, the system includes:
the corner point calculating module 501 is used for calculating corner point information of an image according to the collected image of the known defect-free liquid crystal screen;
a non-defect statistical characteristic module 502, configured to calculate a statistical characteristic of a non-defect corner position according to the corner information;
and the defect position calculating module 503 is configured to calculate a defect position of the screen to be detected by using a nearest neighbor algorithm according to the statistical characteristic of the non-defect corner positions.
The nearest neighbor algorithm-based liquid crystal screen defect detection system provided by the embodiment of the invention and the nearest neighbor algorithm-based liquid crystal screen defect detection method provided by the embodiment of the invention have the same inventive concept and have the same beneficial effects as methods adopted, operated or realized by application programs stored by the system.
The embodiment of the invention also provides electronic equipment corresponding to the nearest neighbor algorithm-based liquid crystal screen defect detection method provided by the foregoing embodiment, so as to execute the nearest neighbor algorithm-based liquid crystal screen defect detection method. The embodiments of the present invention are not limited.
Referring to fig. 5, a schematic diagram of an electronic device according to some embodiments of the invention is shown. As shown in fig. 5, the electronic device 2 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the method for detecting a defect of a liquid crystal screen based on a nearest neighbor algorithm provided by any one of the foregoing embodiments of the present invention when executing the computer program.
The Memory 201 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic equipment provided by the embodiment of the invention and the liquid crystal screen defect detection method based on the nearest neighbor algorithm provided by the embodiment of the invention have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
The embodiment of the present invention further provides a computer-readable storage medium corresponding to the method for detecting a defect of a liquid crystal screen based on a nearest neighbor algorithm provided in the foregoing embodiment, please refer to fig. 6, which illustrates the computer-readable storage medium being an optical disc 30 having a computer program (i.e., a program product) stored thereon, where the computer program, when being executed by a processor, will execute the method for detecting a defect of a liquid crystal screen based on a nearest neighbor algorithm provided in any of the foregoing embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memories (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present invention and the method for detecting a defect of a liquid crystal screen based on a nearest neighbor algorithm provided by the embodiment of the present invention have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a virtual machine creation system according to embodiments of the present invention. The present invention may also be embodied as apparatus or system programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A liquid crystal screen defect detection method based on a nearest neighbor algorithm is characterized by comprising the following steps:
calculating corner information of the image according to the acquired image of the known defect-free liquid crystal screen;
calculating the statistical characteristic of the position of the non-defect corner according to the corner information;
and calculating the defect position of the screen to be detected by utilizing a nearest neighbor algorithm according to the statistical characteristics of the positions of the non-defect angular points.
2. The method for detecting the defects of the liquid crystal screen based on the nearest neighbor algorithm as claimed in claim 1, wherein when the corner point information of the image is calculated according to the collected image of the liquid crystal screen with known defects, the corner point calculating method comprises: a Harris corner-based algorithm, a FAST-based corner detection algorithm, a SIFT-based corner detection algorithm, or a SURF-based corner detection algorithm.
3. The method for detecting the defects of the liquid crystal screen based on the nearest neighbor algorithm as claimed in claim 2, wherein the step of calculating the statistical characteristics of the positions of the non-defective corner points according to the corner point information comprises the steps of:
for non-defective corner points, digging out n multiplied by n corner point region images by taking the corner points as centers, and calculating the statistical characteristics of the corner points, wherein the statistical characteristics comprise:
average gray value of the image of the angular point region;
mean square error of the corner region image;
the gray value sum of the image after the first-order partial derivative along the X direction of the angular point region image;
the gray value sum of the image after the first-order partial derivative along the Y direction of the angular point region image;
the gray value sum of the second derivative of the image of the angular point region;
and calculating 5 statistical characteristics of each non-defect corner point, normalizing, and recording to form a statistical characteristic table.
4. The method for detecting the defects of the liquid crystal screen based on the nearest neighbor algorithm according to claim 3, wherein the calculating the defect position of the screen to be detected by using the nearest neighbor algorithm according to the statistical characteristics of the positions of the non-defect corner points comprises:
after 5 statistical characteristic values of the corner points of the screen to be detected are calculated, a non-defect corner point area closest to the corner points is found out according to a Euclidean distance calculation formula, and if the distance is smaller than or equal to a threshold value, the corner point is considered to be a normal corner point and not a defect corner point; and if the distance is greater than the threshold value, the corner point is considered as a defect corner point.
5. The method for detecting the defects of the liquid crystal screen based on the nearest neighbor algorithm as claimed in claim 3, wherein the average gray value of the image of the corner region is calculated as follows:
wherein i, j represents the horizontal and vertical coordinates of the angular point region image, n is the width and height of the angular point region image, and f (i, j) represents the gray value of the angular point region image at the coordinates (i, j).
6. The method for detecting the defects of the liquid crystal screen based on the nearest neighbor algorithm as claimed in claim 5, wherein the mean square error of the image of the corner region is calculated by the following formula:
7. the method for detecting defects of liquid crystal screens based on nearest neighbor algorithm as claimed in claim 5, wherein the sum of gray values of images after the first-order partial derivative along X direction of the image of the corner region is as follows:
wherein x, y represents the horizontal and vertical coordinates of the corner region image, and f (x, y) represents the gray value of the corner region image at the coordinates (x, y).
8. The method for detecting defects of liquid crystal screens based on nearest neighbor algorithm as claimed in claim 7, wherein the summation of gray values of images after the first-order partial derivatives along Y direction of the image of the corner region is as follows:
the sum of gray values after the second derivative of the image in the corner region is calculated by a laplacian operator, wherein the laplacian operator formula is as follows:
wherein:
9. the method for detecting the defects of the liquid crystal screen based on the nearest neighbor algorithm as claimed in claim 8, wherein the normalized formula is as follows:
where r (x) is the calculated statistical property, min (r) represents the minimum of the 5 properties, max (r) represents the maximum of the 5 properties, and norm (x) is the normalized value.
10. A liquid crystal screen defect detection system based on nearest neighbor algorithm is characterized by comprising:
the corner point calculation module is used for calculating corner point information of the image according to the collected image of the known defect-free liquid crystal screen;
the non-defect statistical characteristic module is used for calculating the statistical characteristic of the position of a non-defect corner according to the corner information;
and the defect position calculation module is used for calculating the defect position of the screen to be detected by utilizing a nearest neighbor algorithm according to the statistical characteristics of the positions of the non-defect angular points.
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