TWI843045B - Image processing and detection method, computer device, and storage medium - Google Patents

Abstract

The present application provides an image processing and detection method, a computer device, and a storage medium. The image processing and detection method includes: acquiring an image to be detected, and correcting the image to be detected; performing a flaw enhancement processing on the corrected image, including: performing a median filtering processing on the corrected image; performing a contrast adjustment on the corrected image after the median filtering processing; performing a bilateral filtering processing on the corrected image after the contrast adjustment; performing a flaw detection on the corrected image after the flaw enhancement processing and obtaining a detection result. This application can assist in detecting whether there is any abnormality in the image, and improving the accuracy of image processing and detection.

Description

Image processing and detection method, computer device and storage medium

The present invention relates to the field of product testing, and in particular to an image processing and testing method, a computer device, and a storage medium.

In the actual industrial production process, some products have inevitable scratches or dust defects on their surfaces. Even if a high-resolution camera is used to shoot the product, some defects may not be obvious in different light sources, resulting in reduced accuracy in product defect detection.

In view of the above, it is necessary to provide an image processing and detection method, a computer device and a storage medium that can assist in detecting product abnormalities to solve the above problems.

The image processing and detection method includes: obtaining an image to be detected, correcting the image to be detected; performing defect enhancement processing on the corrected image to be detected, including: performing median filtering processing on the corrected image to be detected; adjusting the contrast of the image to be detected after the median filtering processing; performing bilateral filtering processing on the image to be detected after the contrast adjustment; performing defect detection on the image to be detected after the defect enhancement processing, and obtaining a detection result.

Optionally, the correction of the image to be detected includes: obtaining a flawless image, and using the flawless image as a reference image to perform position correction on the image to be detected.

Optionally, the median filtering process for the corrected image to be detected includes: In the corrected image to be detected, by sliding a preset sliding window, updating the pixel value of each point in the corrected image to be detected to the median value of the pixel value in the sliding window; performing edge processing on the pixel value at the edge position of the corrected image to be detected.

Optionally, the contrast adjustment of the image to be detected after the median filtering process includes: obtaining a brightness bar graph of the image to be detected after the median filtering process; setting an equalization threshold K , and updating the brightness bar graph according to the equalization threshold K ; and adjusting the contrast of the image to be detected after the median filtering process using a bar graph equalization method according to the updated brightness bar graph.

Optionally, the horizontal axis of the brightness bar graph is the pixel value v , and the vertical axis of the brightness bar graph is the number of pixels y _v corresponding to the pixel value v in the image to be detected after the median filtering process.

獲得更新後的亮度長條圖，所述更新後的亮度長條圖的橫軸為圖元值v，所述更新後的亮度長條圖的縱軸為對應圖元值v的圖元數量y' _v 。 Optionally, updating the brightness bar graph according to the equalization threshold K comprises: determining the number of pixels y _v in the brightness bar graph that are greater than the equalization threshold K , using the formula

An updated brightness bar graph is obtained, wherein the horizontal axis of the updated brightness bar graph is the pixel value v , and the vertical axis of the updated brightness bar graph is the number of pixels y' _v corresponding to the pixel value v .

，其中，round表示取整函數，M表示所述中值濾波處理後的待檢測圖像的寬的圖元的數量，N表示所述中值濾波處理後的待檢測圖像的高的圖元的數量。 Optionally, adjusting the contrast of the image to be detected after the median filtering process by using the histogram equalization method according to the updated brightness histogram includes: calculating a cumulative distribution function cdf ( v ), updating the pixel value v by using the cumulative distribution function cdf ( v ), and obtaining an updated pixel value k ( v ), and the formula used is:

, wherein round represents a rounding function, M represents the number of wide pixels of the image to be detected after the median filtering process, and N represents the number of high pixels of the image to be detected after the median filtering process.

Optionally, the defect detection of the image to be detected after defect enhancement processing to obtain the detection result includes: using a pre-trained image processing and detection model to perform defect detection on the image to be detected after defect enhancement processing to obtain a defect value of the image to be detected after defect enhancement processing; judging whether the defect value exceeds a preset defect threshold range; when the defect value exceeds the defect value range, determining that the image to be detected is a defective image; or when the defect value does not exceed the defect value range, determining that the image to be detected is a non-defective image.

The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the image processing and detection method is implemented.

The computer device includes a memory and at least one processor, wherein at least one instruction is stored in the memory, and when the at least one instruction is executed by the at least one processor, the image processing and detection inspection method is implemented.

Compared with the prior art, the image processing and detection method, computer device and storage medium can enhance the appearance of defects on the image without affecting the normal sample, so as to improve the accuracy of defect detection.

3:Computer equipment

32: Processor

31: Storage

30: Image processing and detection system

S1~S3: Steps

S20~S22: Steps

S30~S33: Steps

In order to more clearly illustrate the technical solutions in the embodiments of this application or the known technology, the drawings required for the embodiments or the known technology description will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without creative labor.

Figure 1 is a flow chart of the image processing and detection method provided by the embodiment of this application.

Figure 2 is a diagram of the architecture of the computer device provided in the embodiment of this application.

Figure 3 is a flow chart of step S2 provided in the embodiment of this application.

Figure 4 is an example diagram of the image processing and detection method provided by the embodiment of this application.

Figure 5 is a flow chart of step S3 provided in the embodiment of this application.

In order to more clearly understand the above-mentioned purpose, features and advantages of this application, the following is a detailed description of this application in conjunction with the attached drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of this application and the features in the embodiments can be combined with each other.

In the following description, many specific details are explained to facilitate a full understanding of this application. The embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative labor are within the scope of protection of this application.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application. The terms used in this specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

See Figure 1, which is a flowchart of image processing and detection provided in the embodiment of this application.

In this embodiment, the image processing and detection method can be applied to a computer device. For a computer device that needs to perform image processing and detection, the image processing and detection functions provided by the method of this application can be directly integrated on the computer device, or run on the computer device in the form of a software development kit (SDK).

As shown in Figure 1, the image processing and detection method specifically includes the following steps. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

Step S1, the computer device obtains the image to be detected and calibrates the image to be detected.

In one embodiment, the computer device can respond to user input to obtain the image to be detected, which may include multiple images, and then the operation on the image to be detected includes the operation on each of the multiple images. The user input can be the transmission of the image, such as uploading or downloading, and the image to be detected is input through an interface or interface.

In addition, the computer device can also pre-store the image to be detected in the memory of the computer device, or pre-store the image to be detected in other devices that are connected to the computer device. The image to be detected can be an image taken of a certain printed material that needs to be detected (such as a cosmetics manual, etc.).

In one embodiment, the correction of the image to be detected includes: obtaining a flawless image, and using the flawless image as a reference image to perform position correction on the image to be detected.

The computer device can respond to user input and obtain a flawless image, and use the flawless image as a reference image when performing image correction on the image to be detected. The computer device can also pre-store the flawless image in a memory of the computer device, or pre-store the flawless image in other devices that are connected to the computer device in communication.

In this embodiment, the flawless image may be a standard sample (Golden Sample) image of the printed product, that is, an image taken of the printed product without any flaws.

In one embodiment, performing image correction on the image to be detected includes using scale-invariant feature transform (SIFT) technology to perform geometric position correction on the position of the image to be detected.

The geometric correction of the position of the image to be detected by using SIFT technology includes: obtaining multiple first feature points in the image to be detected (for example, points where the texture in the image to be detected changes dramatically, corner points, intersections of straight lines, and isolated points in a simple area, etc.) and multiple second feature points in the flawless image (for example, points where the texture in the flawless image changes dramatically, corner points, intersections of straight lines, and isolated points in a simple area, etc.);

By using a matching matrix calculation method, a matching relationship is established between each first feature point and each second feature point to obtain a plurality of mutually matching first feature point pairs; a random sample consensus (RANSAC) algorithm is used to eliminate the first feature point pairs with incorrect matching from the plurality of first feature point pairs to obtain the first feature point pairs with correct matching; based on the first feature point pairs with correct matching, a first correction matrix of the image to be detected is calculated; based on the first correction matrix, the image to be detected is corrected. It should be noted that in this embodiment, each first feature point pair is a combination of mutually matching first feature points and second feature points.

It should be noted that the size of the image to be detected after correction is the same as the flawless image.

Step S2: The computer device performs defect enhancement processing on the calibrated image to be detected.

In one embodiment, the detailed process of performing defect enhancement processing on the corrected image to be detected includes steps 20 to 22 as shown in FIG3.

Step 20: Perform median filtering on the corrected image to be detected.

In one embodiment, the median filtering process for the corrected image to be detected includes: in the corrected image to be detected, by sliding a preset sliding window, updating the pixel value of each point in the corrected image to be detected to the median value of the pixel value in the sliding window; performing edge processing on the pixel value at the edge position of the corrected image to be detected.

The computer device can set the shape of the sliding window, for example, the shape of the sliding window can be set to a square, or it can be set to a linear, circular, cross, ring, etc. The size of the sliding window can be fixed or changeable, and the sliding window can accommodate an odd number of graphics elements.

By sliding the sliding window over the corrected image to be detected (for example, from left to right, from top to bottom), the pixel value of each point in the corrected image to be detected is updated to the median value of the pixel value in the sliding window to eliminate isolated noise pixels (for example, pepper and salt noise, pulse noise, etc.) in the corrected image to be detected.

When edge processing is performed on the pixel values at the edge positions of the corrected image to be detected (for example, the four edges of a rectangular image), the computer device can update the pixel values at the edge positions to the values of the pixel closest to them.

As shown in FIG4 , the corrected image 4A to be detected is subjected to median filtering to obtain image 4B.

Step 21: Adjust the contrast of the image to be detected after median filtering.

In one embodiment, the contrast adjustment of the image to be detected after the median filtering process includes: obtaining a brightness bar graph of the image to be detected after the median filtering process; setting an equalization threshold K , and updating the brightness bar graph according to the equalization threshold K ; and adjusting the contrast of the image to be detected after the median filtering process using a bar graph equalization method according to the updated brightness bar graph.

The horizontal axis of the brightness bar graph is the pixel value v , and the vertical axis of the brightness bar graph is the number of pixels y _v corresponding to the pixel value v in the image to be detected after the median filtering process.

y _v 獲得更新後的亮度長條圖，所述更新後的亮度長條圖的橫軸為圖元值v，所述更新後的亮度長條圖的縱軸為對應圖元值v的圖元數量y' _v 。 The updating of the brightness bar graph according to the equalization threshold K comprises: determining the number of picture elements y _v in the brightness bar graph that are greater than the equalization threshold K , using the formula

y _v obtains an updated brightness bar graph, wherein the horizontal axis of the updated brightness bar graph is the pixel value v , and the vertical axis of the updated brightness bar graph is the number of pixels y' _v corresponding to the pixel value v .

The selection of the cutoff threshold K is proportional to the total number of picture elements in the image to be detected after the median filtering process. For example, when the number of wide picture elements in the image to be detected after the median filtering process is 640, and the number of high picture elements in the image to be detected after the median filtering process is 480, the total number of picture elements in the image to be detected after the median filtering process is 640×480=307200, and the cutoff threshold K can be 2000.

，其中，round表示取整函數，M表示所述中值濾波處理後的待檢測圖像的寬的圖元的數量，N表示所述中值濾波處理後的待檢測圖像的高的圖元的數量。 The step of adjusting the contrast of the image to be detected after the median filtering process by using the histogram equalization method according to the updated brightness histogram includes: calculating the cumulative distribution function cdf ( v ), updating the pixel value v by using the cumulative distribution function cdf ( v ), and obtaining the updated pixel value k ( v ), wherein the formula used is:

, wherein round represents a rounding function, M represents the number of wide pixels of the image to be detected after the median filtering process, and N represents the number of high pixels of the image to be detected after the median filtering process.

As shown in FIG4 , the image 4B to be detected after median filtering is subjected to contrast adjustment to obtain image 4C.

Step 22: Perform bilateral filtering on the image to be detected after adjusting the contrast.

，

，其中p表示目標圖元，S表示濾波視窗(例如上述的滑動視窗)中的圖元群，即目標圖元的周圍的圖元群，q表示目標圖元的周圍的圖元群中的一個圖元，I _q 表示q的圖元值，W _p 表示濾波視窗內每個圖元值的權重和，用於權重的歸一化。 In one embodiment, bilateral filtering is an edge protection filtering method. Commonly used Gaussian filtering is based on normal distribution, and the weight is calculated according to the distance between the pixel point in the convolution template and the target pixel point, thereby achieving a blurring effect, and blurring the edge details of the image while smoothing. In order to retain the edge details, the bilateral filter adds the grayscale weight components of the pixel point in the convolution template and the target pixel point. That _{is ,} the pixel point color difference weight Gr and the spatial distance weight Gs are added to obtain _{the bilateral filter Ip} , which is specifically expressed as:

,

, where p represents the target pixel, S represents the pixel group in the filtering window (such as the sliding window mentioned above), that is, the pixel group surrounding the target pixel, q represents a pixel in the pixel group surrounding the target pixel, I _q represents the pixel value of q , and W _p represents the weighted sum of each pixel value in the filtering window, which is used for weight normalization.

In the flat area of the image to be detected after the contrast adjustment, the Gr value of each pixel point in the bilateral filter is similar, and the spatial distance weight G _S dominates the filtering effect. In the edge area of the image to be detected after the contrast adjustment, the Gr _{value on} the same side of the edge is similar and much larger than the Gr value on the other side of the edge. At this time, the weight of the _pixel point on the other side has almost no effect on the filtering result, and the edge information is protected, showing a certain degree of self-adjustment.

As shown in FIG4 , the image 4C to be detected after contrast adjustment is subjected to bilateral filtering to obtain image 4D.

Step S3: The computer device performs defect detection on the image to be detected after defect enhancement processing to obtain the detection result.

In one embodiment, the defect detection is performed on the image to be detected after defect enhancement processing, and the detailed process of obtaining the detection result can refer to the following process shown in Figure 5.

Step S30, using the pre-trained image detection model to perform defect detection on the image to be detected after the defect enhancement processing, and obtain the defect value of the image to be detected after the defect enhancement processing.

In one embodiment, the image to be detected after defect enhancement processing can be divided into a training set and a verification set (for example, divided in a ratio of 7:3), and the training set is used as a training sample to train a neural network (for example, a convolutional neural network) to obtain a detection model; and it is determined whether the detection model meets the preset requirements (for example, the training process of the detection model reaches 1000 Epochs), and the detection model that meets the preset requirements is used as the image detection model. The verification set is input into the image detection model for detection to obtain the defect value, and the value range of the defect value can be [0, 1].

In other embodiments, the computer device can also obtain the defect value obtained by the AOI detection equipment performing defect detection on the image to be detected after the defect enhancement processing.

In step S31, the computer device determines whether the defect value exceeds the range of the preset defect threshold value; when the defect value exceeds the range of the defect value, step S32 is executed; and when the defect value does not exceed the range of the defect value, step S33 is executed.

In one embodiment, the preset defect threshold value may be in the range of greater than 0 and less than or equal to 0.05.

Step S32, the computer device determines that the image to be detected is a defective image.

In one embodiment, when it is determined that any image to be detected after defect enhancement processing in the verification set is a defective image, the original image to be detected corresponding to any image to be detected after defect enhancement processing (i.e., the image to be detected obtained in step S1) is determined to be a defective image.

Step S33, the computer device determines that the image to be detected is a flawless image.

In one embodiment, when it is determined that any image to be detected after defect enhancement processing in the verification set is a flawless image, it is determined that the original image to be detected (i.e., the image to be detected obtained in step S1) corresponding to any image to be detected after defect enhancement processing is a flawless image.

The above Figure 1 introduces the image processing and detection method of this application in detail. The following is combined with Figure 2 to introduce the hardware device architecture for implementing the image processing and detection method.

It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

Refer to FIG. 2, which is a schematic diagram of the structure of a computer device provided in a preferred embodiment of the present application. In the preferred embodiment of the present application, the computer device 3 includes a memory 31 and at least one processor 32. Those skilled in the art should understand that the structure of the computer device shown in FIG. 2 does not constitute a limitation of the present embodiment, and can be a bus structure or a star structure. The computer device 3 can also include more or less other hardware or software than shown in the figure, or different component layouts.

In some embodiments, the computer device 3 includes a terminal capable of automatically performing numerical calculations and/or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, dedicated integrated circuits, programmable gate arrays, digital signal processors, and embedded devices, etc.

It should be noted that the computer device 3 is only an example. Other existing or future electronic products that are suitable for this application should also be included in the protection scope of this application and included here by reference.

In some embodiments, the memory 31 is used to store program codes and various data, such as the image processing and detection system 30 installed in the computer device 3, and to achieve high-speed and automatic access to programs or data during the operation of the computer device 3. The storage device 31 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable storage medium that can be used to carry or store data.

In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital signal processing chips, graphics processors, and combinations of various control chips. The at least one processor 32 is the control core (Control Unit) of the computer device 3, and uses various interfaces and lines to connect the various components of the entire computer device 3, and executes or executes the programs or modules stored in the memory 31, and calls the data stored in the memory 31 to execute various functions of the computer device 3 and process data, such as executing the image processing and detection functions shown in Figure 1.

In some embodiments, the image processing and detection system 30 runs in a computer device 3. The image processing and detection system 30 may include a plurality of functional modules composed of program code segments. The program code of each program segment in the image processing and detection system 30 may be stored in a memory 31 of the computer device 3 and executed by at least one processor 32 to implement the image processing and detection functions shown in FIG1 .

Although not shown, the computer device 3 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 32 through a power management device, so as to manage charging, discharging, and power consumption through the power management device. The power source may also include one or more DC or AC power sources, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 3 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be elaborated here.

It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including several instructions for enabling a computer device (which can be a server, a personal computer, etc.) or a processor to execute a part of the method described in each embodiment of the present application.

In a further embodiment, in conjunction with FIG. 2 , the at least one processor 32 can execute the operating device of the computer device 3 and various installed application programs (such as the image processing and detection system 30 ), program codes, etc., for example, the various modules mentioned above.

The memory 31 stores program codes, and the at least one processor 32 can call the program codes stored in the memory 31 to execute related functions. For example, each module is a program code stored in the memory 31 and executed by the at least one processor 32, thereby realizing the functions of each module to achieve the purpose of image processing and detection shown in Figure 1.

In one embodiment of the present application, the memory 31 stores one or more instructions (i.e., at least one instruction), and the at least one instruction is executed by the at least one processor 32 to achieve the purpose of image processing and detection shown in FIG1.

In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of this embodiment.

In addition, each functional module in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional modules.

It is obvious to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is limited by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "including" does not exclude other units or, and the singular does not exclude the plural. Multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, not to indicate any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of this application and are not limiting. Although this application is described in detail with reference to the above preferred embodiments, ordinary technicians in this field should understand that the technical solution of this application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of this application.

S1~S3: Steps

Claims (9)

An image processing and detection method is applied to a computer device, wherein the method comprises: obtaining an image to be detected, correcting the image to be detected; performing defect enhancement processing on the corrected image to be detected, including: performing median filtering processing on the corrected image to be detected; adjusting the contrast of the image to be detected after the median filtering processing, including: obtaining the image to be detected after the median filtering processing; Detect the brightness bar graph of the image; set the equalization threshold K, and update the brightness bar graph according to the equalization threshold K; adjust the contrast of the image to be detected after the median filtering processing by using the bar graph equalization method according to the updated brightness bar graph; perform bilateral filtering processing on the image to be detected after the contrast adjustment; perform defect detection on the image to be detected after the defect enhancement processing, and obtain the detection result. As described in claim 1, the image processing and detection method, wherein the correction of the image to be detected includes: obtaining a flawless image, and using the flawless image as a reference image to perform position correction on the image to be detected. The image processing and detection method as described in claim 1, wherein the median filtering process on the corrected image to be detected includes: in the corrected image to be detected, by sliding a preset sliding window, updating the pixel value of each point in the corrected image to be detected to the median value of the pixel value in the sliding window; performing edge processing on the pixel value at the edge position of the corrected image to be detected. The image processing and detection method as described in claim 1, wherein the horizontal axis of the brightness bar graph is the pixel value v , and the vertical axis of the brightness bar graph is the number of pixels yv corresponding to the pixel value v in the image to be detected _after the median filtering process. The image processing and detection method as claimed in claim 4, wherein updating the brightness bar graph according to the equalization threshold K comprises: determining the number of pixels y _v in the brightness bar graph that are greater than the equalization threshold K , using the formula

An updated brightness bar graph is obtained, wherein the horizontal axis of the updated brightness bar graph is the pixel value v , and the vertical axis of the updated brightness bar graph is the number of pixels y' _v corresponding to the pixel value v . The image processing and detection method as described in claim 4, wherein the adjusting the contrast of the image to be detected after the median filtering processing by using the histogram equalization method according to the updated brightness histogram comprises: calculating the cumulative distribution function cdf ( v ), updating the pixel value v by using the cumulative distribution function cdf ( v ), and obtaining the updated pixel value k ( v ), and the formula used is:

Wherein, round represents a rounding function, M represents the number of wide pixels of the image to be detected after the median filtering process, and N represents the number of high pixels of the image to be detected after the median filtering process. The image processing and detection method as described in claim 3, wherein the defect detection is performed on the image to be detected after defect enhancement processing to obtain the detection result, including: using a pre-trained image processing and detection model to perform defect detection on the image to be detected after defect enhancement processing to obtain the defect value of the image to be detected after defect enhancement processing; judging whether the defect value exceeds the range of a preset defect threshold value; when the defect value exceeds the range of the defect value, determining that the image to be detected is a defective image; or when the defect value does not exceed the range of the defect value, determining that the image to be detected is a non-defective image. A computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the image processing and detection method as described in any one of claims 1 to 7 is implemented. A computer device, wherein the computer device includes a memory and at least one processor, wherein at least one instruction is stored in the memory, and when the at least one instruction is executed by the at least one processor, the image processing and detection method as described in any one of claims 1 to 7 is implemented.

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