Defect detection method and device
The defect detection method uses FFT and IFFT to suppress background texture and enhance contrast, addressing the challenge of small, low-contrast defects in display panels, thereby improving detection accuracy and efficiency.
Patent Information
- Application Number
- CN202411047911.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-31
- Publication Date
- 2024-12-31
AI Technical Summary
Existing defect detection methods in display panels face challenges with small, low-contrast defects that are easily submerged by the background, making accurate judgment of defect boundaries difficult due to factors like small size, low contrast, and fence-like texture interference.
A defect detection method involving amplitude spectrum adjustment through fast Fourier transform (FFT) and inverse FFT (IFFT) to suppress background texture, combined with contrast enhancement and image recognition using pre-trained neural networks to enhance defect visibility and improve detection accuracy.
The method effectively enhances defect visibility and improves detection accuracy by removing background interference, reducing manual labeling difficulty and enhancing recognition efficiency.
Smart Images
Figure CN119228723A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of equipment detection, and in particular to a defect detection method and device. Background Art
[0002] During the quality inspection of display panels, various defects will be detected, such as bright spots or bright spots, dark spots or dark spots, Mura (the phenomenon of uneven display brightness causing various traces), foreign matter, dirt, etc. Since different customers have different tolerances for various defects and different card control precision, it is necessary to finely segment various defects. Summary of the invention
[0003] The present disclosure provides a defect detection method, device, electronic device and storage medium to at least solve the above technical problems existing in the prior art.
[0004] According to a first aspect of the present disclosure, a defect detection method is provided, the method comprising:
[0005] Obtaining an image to be processed corresponding to the object to be detected;
[0006] According to the amplitude spectrum of the image to be processed, the amplitude value of the image to be processed is adjusted to obtain the image to be detected; and the defect detection result of the object to be detected is determined based on the image to be detected.
[0007] According to a second aspect of the present disclosure, a defect detection device is provided, the device comprising:
[0008] An acquisition module is used to acquire an image to be processed corresponding to the object to be detected;
[0009] A first processing module, configured to adjust the amplitude value of the image to be processed according to the amplitude spectrum of the image to be processed to obtain an image to be detected;
[0010] The second processing module is used to determine the defect detection result of the object to be detected based on the image to be detected.
[0011] According to a third aspect of the present disclosure, there is provided an electronic device, including:
[0012] at least one processor; and
[0013] a memory communicatively connected to the at least one processor; wherein,
[0014] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.
[0015] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which:
[0017] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.
[0018] FIG. 1( a ) is a defect schematic diagram provided in an embodiment of the present disclosure;
[0019] FIG1( b ) is another defect schematic diagram provided by an embodiment of the present disclosure;
[0020] FIG1( c ) is another defect schematic diagram provided in an embodiment of the present disclosure;
[0021] Figure 2 A schematic diagram of an implementation flow of a defect detection method provided in an embodiment of the present disclosure;
[0022] Figure 3 A schematic diagram of the effect of a defect detection method provided by an application embodiment of the present disclosure;
[0023] Figure 4 A structural schematic diagram of a defect detection device provided in an embodiment of the present disclosure;
[0024] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0025] In order to make the purpose, features, and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.
[0026] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.
[0027] In the following description, the terms "first\second" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein.
[0028] Unless otherwise defined, all technical and scientific terms used in this disclosure have the same meaning as those commonly understood by those skilled in the art to which this disclosure belongs. The terms used in this disclosure are only for the purpose of describing the embodiments of this disclosure and are not intended to limit this disclosure.
[0029] It should be understood that in the various embodiments of the present disclosure, the size of the serial number of each implementation process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
[0030] In related technologies, the defect segmentation effect is affected by the following factors:
[0031] (1) The defect itself is too small, with the smallest defect being only one pixel, as shown in Figure 1(a);
[0032] (2) The defect contrast is too low. The pixel values of the defect and the background differ by only a few pixels. The camera used is a black and white camera, so it is difficult for the human eye to accurately distinguish the defect from the background, as shown in Figure 1(b).
[0033] (3) Due to the high accuracy of the camera (e.g., 150 million pixels, the length and width of the image are both greater than 10,000 pixels), and the different brightness of different pixel grids, the background of the captured image presents a fence-like texture, and low-contrast defects are easily submerged by the background, as shown in Figure 1(c).
[0034] The above factors make it very difficult to accurately judge the boundaries of defects, which brings serious difficulties to manual labeling and algorithm learning.
[0035] In the related art, although histogram equalization, contrast stretching and other methods can be used to enhance low-contrast defect images, this will also enhance the background to the same extent and cannot change the problem of defect points being interfered by the background.
[0036] Based on this, the present disclosure provides a defect detection method, such as Figure 2 As shown, the method includes:
[0037] Step 201, obtaining an image to be processed corresponding to the object to be detected;
[0038] Step 202: adjusting the amplitude value of the image to be processed according to the amplitude spectrum of the image to be processed to obtain an image to be detected;
[0039] Step 203: Determine a defect detection result of the object to be detected based on the image to be detected.
[0040] In some embodiments, the step of obtaining an image to be processed corresponding to the object to be detected includes:
[0041] An image acquisition device is used to acquire an image of the display panel to be detected to obtain the image to be processed.
[0042] Here, the method can be applied to any intelligent electronic device, such as a computer, a processor, a tablet computer, etc. The intelligent electronic device has or is connected to an image acquisition device, which is used to acquire images, such as a camera.
[0043] Here, the method can be used to detect defects in display panels, and the display panels include but are not limited to: liquid crystal display (LCD), capacitive touch screen (Capacitive Touch Screen), organic light emitting diode (OLED) screen, etc.
[0044] Of course, the method can also be applied to other defect detection scenarios, which are not limited here.
[0045] In some embodiments, adjusting the amplitude value of the image to be processed according to the amplitude spectrum of the image to be processed to obtain the image to be detected includes:
[0046] Determining a maximum point of the first amplitude spectrum according to the first amplitude spectrum of the image to be processed;
[0047] Adjusting the amplitude value of the maximum point to obtain a second amplitude spectrum;
[0048] An image to be detected is generated according to the second amplitude spectrum.
[0049] Here, the first amplitude spectrum of the image to be processed is obtained by performing a first processing on the image to be processed; the first processing may be a fast Fourier transform (FFT).
[0050] In the disclosed embodiment, when the fence-like texture of the image background is observed to have a stable periodic distribution, according to Fourier analysis, the spatial domain is periodic and the frequency domain is also discrete, which means that there will be a maximum point in the spectrum of the image. Therefore, it is proposed to suppress the maximum point in the frequency domain and eliminate the periodic texture in the spatial domain, so as to obtain a clean image with the background removed (that is, to obtain the image to be detected).
[0051] In some embodiments, determining the maximum point of the first amplitude spectrum includes:
[0052] Determine a pixel point in the first amplitude spectrum that meets a target condition as the maximum value point;
[0053] The target conditions include at least one of the following:
[0054] A first condition, wherein the first condition is that: a first amplitude value of the pixel point is greater than a first mean value, and a difference between the first amplitude value and the first mean value exceeds a first threshold value; and the first mean value is a mean value of neighboring pixel points in a horizontal direction of the pixel point;
[0055] The second condition is: the first amplitude value is greater than the second mean, and the difference between the first amplitude value and the second mean exceeds a second threshold; the second mean is the mean of neighboring pixel points in the vertical direction of the pixel point.
[0056] Here, whether it is a maximum point is determined by comparing the first amplitude value of the pixel point with the average amplitude value of the neighboring pixel points.
[0057] Specifically, the horizontal direction may be understood as the X-axis direction, and the vertical direction may be understood as the Y-axis direction.
[0058] The neighborhood pixels are divided into neighborhood pixels in the vertical (Y-axis) direction and neighborhood pixels in the horizontal (X-axis) direction, and are compared with the amplitude mean values of the neighborhood pixels in the two directions to determine whether they are maximum points.
[0059] In some embodiments, the adjusting the amplitude value of the maximum value point includes one of the following:
[0060] Modifying the first amplitude value of the maximum point to the first mean value;
[0061] Modify the first amplitude value of the maximum point to the second mean value;
[0062] The first amplitude value of the maximum point is modified to be the minimum value between the first mean value and the second mean value.
[0063] Specifically, the method for adjusting the amplitude value may be determined in combination with the conditions that are met, including:
[0064] If only the first condition is met, the first amplitude value of the maximum point is modified to the first mean value, that is, modified to the mean amplitude value of the neighboring pixel points in the horizontal direction of the pixel point;
[0065] If only the second condition is met, the first amplitude value of the maximum point is modified to the second mean value, that is, modified to the mean amplitude value of the neighboring pixel points in the vertical direction of the pixel point;
[0066] If the first condition and the second condition are satisfied at the same time, the first amplitude value of the maximum point is modified to be the minimum value between the first mean value and the second mean value.
[0067] In some embodiments, the method further comprises:
[0068] Performing a first processing on the image to be processed to obtain a first amplitude spectrum and a phase spectrum of the image to be processed;
[0069] Correspondingly, generating an image to be detected according to the second amplitude spectrum includes:
[0070] The second amplitude spectrum and the phase spectrum are subjected to a second processing to obtain the image to be detected.
[0071] Here, the first processing is fast Fourier transform (FFT); the second processing is inverse fast Fourier transform (IFFT, Inverse Fast Fourier Transform).
[0072] That is, by performing fast Fourier transform on the image to be processed, a first amplitude spectrum and a phase spectrum of the image to be processed are obtained;
[0073] After adjusting the amplitude value based on the first amplitude spectrum, a second amplitude spectrum is obtained, and an inverse fast Fourier transform is performed in combination with the phase spectrum to obtain an image to be detected.
[0074] In some embodiments, before determining the defect detection result of the object to be detected based on the image to be detected, the method further includes:
[0075] Performing contrast enhancement on the image to be detected to obtain a contrast-enhanced image to be detected;
[0076] The step of determining the defect detection result of the object to be detected based on the image to be detected includes:
[0077] The defect detection result of the object to be inspected is determined by using the contrast-enhanced image to be inspected.
[0078] Here, by contrast enhancement of the image to be inspected, a cleaner and clearer defect image is obtained as the image to be inspected, thereby improving the effect of subsequent defect detection.
[0079] Contrast enhancement can be performed by contrast stretching. Of course, contrast enhancement can also include but is not limited to the following methods: histogram equalization, local contrast enhancement, Gaussian filter enhancement, etc., which can be selected according to actual needs.
[0080] In some embodiments, determining the defect detection result of the object to be detected based on the image to be detected includes:
[0081] The image to be detected is identified by using an image recognition model to determine a defect detection result of the object to be detected; the defect detection result includes at least one of the following: whether the object to be detected has a target feature and the area of the target feature.
[0082] Accordingly, in some embodiments, the method further comprises:
[0083] Obtain at least one training sample;
[0084] Using at least one training sample to train an image neural network, and obtaining a trained image neural network as an image recognition model;
[0085] The training samples include: sample images and labels corresponding to the sample images;
[0086] The label includes at least one of the following: whether the sample image has a target feature, and the area of the target feature.
[0087] Here, an image recognition model is pre-trained and used to recognize the image to be inspected, thereby obtaining defect detection results of the object to be inspected and improving detection efficiency.
[0088] Training samples need to be obtained during training. Specifically, a sample image to be processed corresponding to the object to be detected is obtained, and the amplitude value of the sample image to be processed is adjusted according to the amplitude spectrum of the sample image to be processed to obtain a sample image. In this way, the defect detection method proposed in combination with the characteristics of the display screen detection image can remove background interference and make defects more obvious, which greatly reduces the difficulty of manual labeling, greatly reduces the difficulty of subsequent model training, and improves the recognition accuracy of the image recognition model.
[0089] In addition, it should be noted that the method of the embodiment of the present disclosure can also be applied in data augmentation scenarios. For example, when modifying the maximum point, it can also be changed to any number between the minimum value and the original value to generate augmented data with controllable background intensity.
[0090] Figure 3 A schematic diagram of the effect of a defect detection method provided by an application embodiment of the present disclosure; Figure 3 In the figure, I represents the image to be processed, A represents the first amplitude spectrum, Φ represents the phase spectrum, FMS represents the frequency maximum suppression (Frequency Maxima Suppression), A' represents the second amplitude spectrum, I' represents the image to be detected, and I' represents the image to be detected after contrast enhancement. Figure 3 The defect detection method is described in detail, including:
[0091] Step 1: Perform fast Fourier transform on the image to be processed I to obtain a spectrum F, which is decomposed into a first amplitude spectrum A and a phase spectrum Φ;
[0092] Step 2: Traverse each pixel point in the first amplitude spectrum A and make the following judgment:
[0093] Whether the amplitude value of the pixel point is greater than the mean value of the pixels in the neighborhood in the horizontal direction (X-axis direction) and exceeds a certain ratio, and,
[0094] Whether the amplitude value of the pixel point is greater than the average value of the pixels in the neighborhood in the vertical direction (Y-axis direction) and exceeds a certain ratio;
[0095] If it exceeds, the pixel point is determined to be a maximum point, and thus the maximum point in the first amplitude spectrum is determined.
[0096] The ratio can be set based on actual needs and is not limited here.
[0097] Step 3, modifying the amplitude value of the maximum point screened in the above step 2 to obtain a modified amplitude spectrum, that is, a second amplitude spectrum A';
[0098] The modification methods may include:
[0099] If it is a maximum point in the horizontal direction, it is modified to the mean value of the neighboring pixels in the horizontal direction;
[0100] If the point is a maximum value in the vertical direction, it is modified to the mean value of the neighboring pixels in the vertical direction;
[0101] If the point is a maximum value in both the horizontal and vertical directions, it is modified to the minimum value of the mean of the neighboring pixels in the horizontal direction and the mean of the neighboring pixels in the vertical direction.
[0102] Step 4: Recombine the second amplitude spectrum A' and the phase spectrum Φ to obtain a spectrum F' after suppressing the maximum point; perform an inverse Fourier transform (IFFT) on the spectrum F' to obtain an image to be detected I' after suppressing the background;
[0103] Step 5: Use the image recognition model to identify the image I' to be inspected and obtain the defect recognition result.
[0104] Here, the contrast of the image to be detected I' can also be stretched as needed to obtain a contrast-enhanced image to be detected I", that is, a cleaner and clearer defect image. Accordingly, the image to be detected I" is recognized using an image recognition model to obtain a defect recognition result.
[0105] Image recognition models can be pre-trained, as described in Figure 2 The method is described in detail and will not be repeated here.
[0106] Figure 4 A structural diagram of a defect detection device provided by an embodiment of the present disclosure; Figure 5 As shown, the device comprises:
[0107] An acquisition module is used to acquire an image to be processed corresponding to the object to be detected;
[0108] A first processing module, configured to adjust the amplitude value of the image to be processed according to the amplitude spectrum of the image to be processed to obtain an image to be detected;
[0109] The second processing module is used to determine the defect detection result of the object to be detected based on the image to be detected.
[0110] In some embodiments, the first processing module is used to determine the maximum point of the first amplitude spectrum according to the first amplitude spectrum of the image to be processed;
[0111] Adjusting the amplitude value of the maximum point to obtain a second amplitude spectrum;
[0112] An image to be detected is generated according to the second amplitude spectrum.
[0113] In some embodiments, the first processing module is used to determine the pixel point that meets the target condition in the first amplitude spectrum as the maximum value point;
[0114] The target conditions include at least one of the following:
[0115] A first condition, wherein the first condition is that: a first amplitude value of the pixel point is greater than a first mean value, and a difference between the first amplitude value and the first mean value exceeds a first threshold value; and the first mean value is a mean value of neighboring pixel points in a horizontal direction of the pixel point;
[0116] The second condition is: the first amplitude value is greater than the second mean, and the difference between the first amplitude value and the second mean exceeds a second threshold; the second mean is the mean of neighboring pixel points in the vertical direction of the pixel point.
[0117] In some embodiments, the first processing module is used to modify the first amplitude value of the maximum point to the first mean value;
[0118] Modify the first amplitude value of the maximum point to the second mean value;
[0119] The first amplitude value of the maximum point is modified to be the minimum value between the first mean value and the second mean value.
[0120] In some embodiments, the first processing module is further used to perform a first processing on the image to be processed to obtain a first amplitude spectrum and a phase spectrum of the image to be processed;
[0121] Correspondingly, generating an image to be detected according to the second amplitude spectrum includes:
[0122] The second amplitude spectrum and the phase spectrum are subjected to a second processing to obtain the image to be detected.
[0123] In some embodiments, the second processing module is further used to perform contrast enhancement on the image to be detected to obtain a contrast-enhanced image to be detected before determining a defect detection result of the object to be detected based on the image to be detected;
[0124] The step of determining the defect detection result of the object to be detected based on the image to be detected includes:
[0125] The defect detection result of the object to be inspected is determined by using the contrast-enhanced image to be inspected.
[0126] In some embodiments, the second processing module is used to identify the image to be inspected using an image recognition model to determine a defect detection result of the object to be inspected; the defect detection result includes at least one of the following: whether the object to be inspected has a target feature and the area of the target feature.
[0127] In some embodiments, the apparatus further comprises: a third processing module, configured to obtain at least one training sample;
[0128] Using at least one training sample to train an image neural network, and obtaining a trained image neural network as an image recognition model;
[0129] The training sample includes: a sample image and a label corresponding to the sample image, and the label includes at least one of the following: whether the sample image has a target feature and an area of the target feature.
[0130] In some embodiments, the acquisition module is used to use an image acquisition device to acquire an image of the display panel to be inspected to obtain the image to be processed.
[0131] It is understandable that when the defect detection device provided in the above embodiment implements the corresponding defect detection method, the above processing can be assigned to different program modules as needed to complete all or part of the above-described processing. In addition, the device provided in the above embodiment and the embodiment of the corresponding method belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.
[0132] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.
[0133] Figure 5 A schematic diagram of the structure of an electronic device 500 provided for an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.
[0134] like Figure 5 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.
[0135] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0136] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as the defect detection method. For example, in some embodiments, the defect detection method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the defect detection method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the defect detection method in any other appropriate manner (e.g., by means of firmware).
[0137] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), integrated systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor.
[0138] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0139] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0140] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).
[0141] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.
[0142] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.
[0143] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.
[0144] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.
[0145] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.
Claims
1. A defect detection method, characterized in that: The method comprises: Obtaining an image to be processed corresponding to the object to be detected; According to the amplitude spectrum of the image to be processed, the amplitude value of the image to be processed is adjusted to obtain the image to be detected; A defect detection result of the object to be inspected is determined based on the image to be inspected.
2. The method according to claim 1, characterized in that According to the amplitude spectrum of the image to be processed, the amplitude value of the image to be processed is adjusted to obtain the image to be detected, including: Determining a maximum point of the first amplitude spectrum according to the first amplitude spectrum of the image to be processed; Adjusting the amplitude value of the maximum point to obtain a second amplitude spectrum; An image to be detected is generated according to the second amplitude spectrum.
3. The method according to claim 2, characterized in that The determining of the maximum point of the first amplitude spectrum comprises: Determine a pixel point in the first amplitude spectrum that meets a target condition as the maximum value point; The target conditions include at least one of the following: A first condition, wherein the first condition is that: a first amplitude value of the pixel point is greater than a first mean value, and a difference between the first amplitude value and the first mean value exceeds a first threshold value; and the first mean value is a mean value of neighboring pixel points in a horizontal direction of the pixel point; The second condition is: the first amplitude value is greater than the second mean, and the difference between the first amplitude value and the second mean exceeds a second threshold; the second mean is the mean of neighboring pixel points in the vertical direction of the pixel point.
4. The method according to claim 3, characterized in that The step of adjusting the amplitude value of the maximum value point includes one of the following: Modifying the first amplitude value of the maximum point to the first mean value; Modify the first amplitude value of the maximum point to the second mean value; The first amplitude value of the maximum point is modified to be the minimum value between the first mean value and the second mean value.
5. The method according to claim 2, characterized in that: The method further comprises: Performing a first processing on the image to be processed to obtain a first amplitude spectrum and a phase spectrum of the image to be processed; Correspondingly, generating an image to be detected according to the second amplitude spectrum includes: The second amplitude spectrum and the phase spectrum are subjected to a second processing to obtain the image to be detected.
6. The method according to claim 1, characterized in that Before determining the defect detection result of the object to be detected based on the image to be detected, the method further includes: Performing contrast enhancement on the image to be detected to obtain a contrast-enhanced image to be detected; The step of determining the defect detection result of the object to be detected based on the image to be detected includes: The defect detection result of the object to be inspected is determined by using the contrast-enhanced image to be inspected.
7. The method according to claim 1, characterized in that The step of determining the defect detection result of the object to be detected based on the image to be detected includes: The image to be detected is identified by using an image recognition model to determine a defect detection result of the object to be detected; the defect detection result includes at least one of the following: whether the object to be detected has a target feature and the area of the target feature.
8. The method according to claim 7, characterized in that The method further comprises: Obtain at least one training sample; Using at least one training sample to train an image neural network, and obtaining a trained image neural network as an image recognition model; The training sample includes: a sample image and a label corresponding to the sample image, and the label includes at least one of the following: whether the sample image has a target feature and an area of the target feature.
9. The method according to claim 1, characterized in that: The step of obtaining the image to be processed corresponding to the object to be detected includes: An image acquisition device is used to acquire an image of the display panel to be detected to obtain the image to be processed.
10. A defect detection device, characterized in that: The device comprises: An acquisition module is used to acquire an image to be processed corresponding to the object to be detected; A first processing module, configured to adjust the amplitude value of the image to be processed according to the amplitude spectrum of the image to be processed to obtain an image to be detected; The second processing module is used to determine the defect detection result of the object to be detected based on the image to be detected.