TW202230281A - Image reinspection method, computer device, and storage medium - Google Patents

Abstract

The present application provides an image reinspection method, a computer device, and a storage medium. The image reinspection method includes obtaining images to be tested and corresponding image information; using an artificial intelligence program to analyze the image information and obtain parameters of the image to be tested; using a pre-trained image retest model to retest the images to be tested based on the parameters and obtaining retest results; uploading the images to be tested with flaws in the retest results to a pre-established image re-inspection system; distributing the images to be tested to designated users using the pre-established image re-inspection system, receiving marks of the designated user for the images to be tested, and feeding back a final test result of the images to be tested according to the marks. This application can assist in the reinspection of printed circuit boards judged as defective by automatic optical inspection equipment, reduce labor costs, and improve inspection efficiency and accuracy.

Description

Image rechecking method, computer device and storage medium

The invention relates to the field of printed circuit board inspection, in particular to an image re-inspection method, a computer device and a storage medium.

Printed circuit boards (PCBs) are mostly inspected by automated optical inspection (AOI) equipment during the manufacturing process, so as to determine whether the PCB contains defects such as offset, missing parts, less tin, and more tin. defective components. Usually, because the PCB judged by AOI equipment as defective products will have a large number of false defects, that is, it is difficult for AOI equipment to accurately detect the defective products that actually only account for a small number of defective products. Manual re-inspection of defective PCBs takes a lot of manpower and time.

In view of the above, it is necessary to provide an image re-inspection method, computer device and storage medium, which can re-inspect printed circuit boards (PCBs) judged as defective products by automatic optical inspection AOI equipment based on computer vision image processing technology, and reduce manual re-inspection. Inspection workload, improve inspection efficiency and accuracy.

The image re-inspection method includes: obtaining an image to be tested and image information of the image to be tested, and the image to be tested is identified as a defective image in at least one detection; The image information of the image to be tested is analyzed to obtain the parameters of the image to be tested; the image rechecking model obtained by pre-training is used to analyze the image to be tested based on the parameters of the image to be tested. Carry out re-inspection to obtain the re-inspection result of the image to be tested; when the re-inspection result indicates that the image to be tested is defective, upload the image to be tested to a pre-established image re-inspection in the computer device. inspection system; distribute the image to be tested to designated users through the image re-inspection system, receive the designated user's mark on the image to be inspected; and give feedback according to the mark of the image to be inspected The final detection result of the image to be tested.

Optionally, the image to be tested is an image of a printed circuit board (PCB), the method uses an automatic optical inspection AOI device to scan the PCB to obtain the image to be tested, and uses the AOI device to scan the image to be tested. Save in the preset path.

Optionally, the method further includes obtaining information of the PCB in the image to be tested.

Optionally, the method further includes: acquiring a preset number of flawless PCB images; performing image processing on the preset number of flawless PCB images; and using the processed flawless PCBs The image trains the neural network to obtain the image rechecking model.

Optionally, the method further includes: using the artificial intelligence program to input the parameters of the image to be tested into the image rechecking model.

Optionally, the method further includes: based on the parameters of the image to be tested, using the image rechecking model to obtain the image to be tested.

Optionally, the method further comprises: using the image re-examination model to input the parameters of the image to be tested and the re-examination result into the artificial intelligence program; and using the artificial intelligence program to The parameters of the image to be tested and the retest results are stored in a pre-established database.

Optionally, the final detection result of the image to be tested includes: the image to be tested is a flawless image, or the image to be tested is a flawed image.

The computer-readable storage medium stores at least one instruction that, when executed by a processor, implements the image rechecking method.

The computer device includes a memory and at least one processor, the memory stores at least one instruction, and the at least one instruction implements the image rechecking method when executed by the at least one processor.

Compared with the prior art, the image re-inspection method, computer device and storage medium can re-inspect PCBs judged as defective products by AOI equipment based on computer vision image processing technology, thereby reducing the workload of manual re-inspection and improving the quality of PCBs. Detection efficiency and accuracy.

In order to more clearly understand the above objects, features and advantages of the present application, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments may be combined with each other in the case of no conflict.

In the following description, many specific details are set forth to facilitate a full understanding of the present application, and the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

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

Referring to FIG. 1 , it is a flowchart of an image rechecking method according to a preferred embodiment of the present application.

In this embodiment, the image re-inspection method can be applied to a computer device (for example, the computer device 3 shown in FIG. 2 ). For a computer device that needs to perform image re-inspection, the computer device can be directly integrated into the computer device. The function for image rechecking provided by the method of the application is run on a computer device in the form of a software development kit (Software Development Kit, SDK).

As shown in FIG. 1 , the image rechecking 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 tested and image information of the image to be tested, and the image to be tested is identified as a defective image in at least one detection.

In one embodiment, the image to be tested is a printed circuit board (Printed Circuit Board, PCB) image, and the method uses an automatic optical inspection (Automated Optical Inspection, AOI) device to scan the PCB to obtain the image to be tested , and use the AOI device to save the image to be tested in a preset path.

It should be noted that the AOI device is a device that can scan products such as PCBs based on optical principles and detect common defects.

In one embodiment, the PCB may come from different production lines, and the production line refers to an assembly line composed of multiple devices required for producing the PCB, and the multiple devices are arranged in the order of the PCB manufacturing processes to form a line production line. For example, the multiple pieces of equipment may include a board loading machine, a solder paste printing machine, a high-speed placement machine, a high-precision placement machine, a reflow oven, etc. The board loading machine is the equipment required for the first process of manufacturing the PCB, and other equipment The reflow oven is placed at the last position of the production line after being placed in sequence on the board machine. The production lines can be distinguished by different numbers, for example, the different production lines can be numbered as 01, 02, etc., with two-digit numbers in order.

In one embodiment, when the AOI equipment is used to scan and detect the PCB produced by the production line, a one-to-one mode is adopted, and one AOI equipment corresponds to one production line. Multiple (eg, 20) AOI devices may be used at the same time to scan PCBs produced by multiple (eg, 20) production lines and detect images of the PCBs to obtain detection results of the images of the PCBs. For example, different AOI devices can be numbered as AOI001, AOI002, etc. in the format of AOI+ ordered three-digit digits, and AOI001 can be placed behind the reflow oven of No. 01 production line, and AOI001 can be used to scan the PCB produced by No. 01 production line At the same time, AOI002 is placed behind the reflow oven of No. 02 production line, and AOI002 is used to scan and test the PCB produced by No. 02 production line.

In one embodiment, the AOI device detects the image of the PCB, and the obtained detection result includes: the image of the PCB is a defect image, and the image of the PCB is a defect-free image. The defect image is an image of a PCB that is judged by the AOI device to contain defects. For example, the AOI device judges an image of a PCB containing defective components such as offset elements, missing parts, less tin, and more tin as defects. image.

In one embodiment, the image to be tested is an image of a PCB detected as a defect image by an AOI device, and the image to be tested is an image to be re-inspected.

In one embodiment, the AOI device only saves the image to be tested, and saves the image to be tested in a preset path. For example, the AOI device numbered AOI001 can store the detected image The to-be-tested image is stored in a pre-established folder Download001 in the local path.

In one embodiment, the computer device obtains the information of the PCB in the image to be tested.

In one embodiment, the information of the PCB includes the product number of the PCB. Specifically, the different PCBs can be numbered as PCB0001, PCB0002, etc. with ordered four-digit digits.

In one embodiment, the information of the PCB further includes information of a production line that produces the PCB. For example, the production line corresponding to the PCB numbered PCB0001 is the production line No. 01.

In one embodiment, the information of the PCB further includes the production time of the PCB, for example, the production time of the PCB numbered PCB0001 is 09:30:05. It should be noted that the time may also include information such as year, month, and day, which will not be described in detail in the example.

In one embodiment, the image information of the image to be tested includes machine information of the AOI device corresponding to the image to be tested. For example, the AOI device numbered AOI001 will use the image of the PCB numbered PCB0001 If it is determined to be an image to be tested, the machine information of the AOI device corresponding to the image to be tested of the PCB numbered PCB0001 is AOI001.

In one embodiment, the image information of the image to be tested further includes the name of the image to be tested. For example, the AOI device numbered AOI001 uses the image of the PCB numbered PCB0001 as the image to be tested. When saving, the image to be tested can be named PCB0001.jpg. It should be noted that when the number of the PCB is used as the name of the image to be tested of the corresponding PCB, the name of the image to be tested does not need to be saved in the image information of the image to be tested, and it is not necessary to store the name of the image to be tested. It will be described in detail.

In one embodiment, the image information of the image to be tested further includes a storage path of the image to be tested. For example, the AOI device numbered AOI001 saves the image to be tested PCB0001.jpg in the folder Download001 .

In one embodiment, the AOI device saves the relevant data of the image to be tested in a preset path, for example, the AOI device numbered AOI001 can use Extensible Markup Language (XML) to The relevant information of the image is marked and defined, and the obtained ".xml" format document is saved in the local path, which is named File001.xml. In other embodiments, the relevant data of the image to be measured may also be saved in a ".txt" format document.

It should be noted that, taking a single AOI device as an example, for example, for the AOI device numbered AOI001, only one folder Download001 can be generated to save the image to be tested, and only one document File001.xml (or File001. txt) to save the relevant information of the image to be tested. In this case, the machine information of the AOI device corresponding to the image to be measured may not be stored in the image information of the image to be measured, and will not be described in detail later.

In one embodiment, the document File001.xml can store the relevant data of multiple (for example, 300) images to be tested obtained by the AOI device numbered AOI001, and the AOI device numbered AOI001 can be treated in the document File001.xml Each information in the relevant data of the image to be measured is marked and defined, and the relevant data of each image to be measured is stored correspondingly. For example, the AOI device numbered AOI001 can save the relevant data of each image to be tested in sequence (for example, from left to right or from top to bottom) in the document File001 according to the time sequence in which each image to be tested was obtained .xml in a column or a row. It should be noted that, when each piece of information in the relevant data of each image to be tested is stored in one row or one column, it may be out of order.

Step S2, the computer device uses an artificial intelligence program to analyze the relevant data of the image to be measured, and obtains the parameters of the image to be measured.

In one embodiment, the computer device can obtain the relevant data of the image to be measured by using an artificial intelligence (Artificial Intelligence, AI) program, parse the relevant data of the image to be measured, and obtain the image to be measured parameter. For example, the file File001.xml is obtained by using the AI program, and the file File001.xml stores the relevant data of the image to be measured obtained by using the AOI device numbered AOI001. The AI program is used to parse the relevant data in the XML format in the document File001.xml into database (eg SQL Server) parameters, and the database parameters are imported into the database for saving. It should be noted that the AI program can acquire multiple documents at the same time and parse the multiple documents at the same time. When the document obtained by the AI program is File001.txt in ".txt" format, File001.txt can be converted into Documents in other formats (such as ".xml" format), and then parse the data in the converted documents.

In one embodiment, the parameters of the image to be tested are the image information of the image to be tested and the information of the PCB. For example, the image of the PCB numbered as PCB0001 is the image to be tested, and its parameters It is: Download001 (the storage path of the PCB image), 01 (the production line of the PCB), PCB0001 (the number of the PCB), 09:30:05 (the production time of the PCB). It should be noted that the format of the parameters of the image to be measured is a database parameter.

In one embodiment, after the computer device uses an AI program to import the parameters of the image to be measured into the database, the database will identify and analyze the parameters of the image to be measured, and analyze the parameters according to preset rules. After the parameters of the image to be tested are sorted. For example, the preset rules may be: parameter 1 is the production time of the PCB, parameter 2 is the production line of the PCB, parameter 3 is the storage path of the image to be measured, and parameter 4 is the number of the PCB. For example, the image of the PCB numbered PCB0001 is the image to be tested, and the SQL Server can sort the parameters of the image to be tested as: 09:30:05, 01, Download001, PCB0001.

In one embodiment, the computer device uses the AI program to input the parameters into the image review model. For example, the AI program invokes an application programming interface (Application Programming Interface, API), and transmits the parameters of the image to be tested into the image rechecking model. It should be noted that when the image re-inspection model receives the parameters of the image to be tested passed in by the AI program by calling the API, it simultaneously receives an instruction to perform image re-inspection to perform image re-inspection.

In step S3, the computer device re-inspects the image to be tested based on the parameters of the image to be tested by using the image re-inspection model obtained by pre-training, and obtains a re-inspection result of the image to be tested.

In one embodiment, the pre-trained image re-inspection model means: a computer device acquires a preset number of flawless PCB images; image processing is performed on the preset number of flawless PCB images ; and using the processed unblemished PCB image to train a neural network to obtain the image rechecking model.

In one embodiment, the preset number of flawless PCB images may be a small number (eg, 200) of flawless PCB images. It should be noted that, due to the low defect rate of the PCB in the actual production process, a large number of defect-free PCB images can be easily obtained. The preset number of flawless PCB images.

In one embodiment, the performing image processing on the preset number of flawless PCB images includes: analyzing the primitives of the flawless PCB images, and calculating the RGB (red) of the images. (R), green (G), blue (B)) and grayscale, divide the area with similar primitives in the image into the same area, separate the image into different areas, locate the components of the PCB in the The position in the image, the area where each component is located is framed, and the image of each area is denoised by using a blurring algorithm, and the unimportant area is eliminated. It should be noted that, the methods used for image processing on the preset number of flawless PCB images are all common methods in the field of image processing, and the specific process will not be repeated here.

In one embodiment, the neural network may be a convolutional neural network (Convolutional Neural Networks, CNN), and the computer device may use the preset number of flawless PCB images to train the CNN to generate the image complex. Check the model. In one embodiment, the image re-inspection model can be copied, installed in the AOI equipment corresponding to each production line, and the to-be-detected images obtained by the corresponding AOI equipment can be detected. In one embodiment, there is no difference between different image review models installed in different AOI devices, and can be copied and installed in different types of AOI devices to run.

In one embodiment, the computer device obtains the image to be tested by using the image rechecking model based on the parameters of the image to be tested.

In one embodiment, the image rechecking model may determine the storage path of the image to be tested based on parameter 3, such as Download001, for example, the folder Download001, and determine the storage path of the image to be tested based on parameter 4, such as PCB0001. The name of the image is, for example, PCB0001.jpg, and the image to be tested is obtained by copying the image in the storage path such as the folder Download001, such as PCB0001.jpg, to the local path.

In one embodiment, the image re-inspection model is substantially an image defect detection model, and the image to be inspected is re-inspected by using the image re-inspection model, that is, the image to be inspected is re-inspected. as flaw detection. The image re-examination model can determine the area of each component in the PCB board in the image to be tested, and compare it with the corresponding area of the flawless image of the PCB. whether the square error reaches a preset threshold) to obtain the re-inspection result of the image to be tested.

In one embodiment, the re-inspection result of the image to be tested includes: the image to be tested is an image with defects, and the image to be tested is an image without defects. It should be noted that, due to the defects of the AOI equipment, only a small part (for example, 4%) of the images to be tested are images with actual defects. The image to be tested can be re-inspected by using an image re-inspection model, and an image that actually has defects can be detected from the image to be tested.

In one embodiment, the computer device can input the parameters of the image to be tested and the re-inspection result into the AI program by using the image re-inspection model; and use the AI program to input the image to be inspected Like parameters and the recheck results are stored in a pre-built database (eg SQL Server).

Step S4, when the re-inspection result indicates that the image to be tested has defects, the computer device uploads the image to be tested to an image re-inspection system pre-established in the computer device.

In one embodiment, the image re-inspection system may acquire the image to be tested based on the parameters of the defective image.

In one embodiment, the image re-inspection system may acquire parameters of the image to be inspected whose re-inspection result is a defect from the database, and display the image to be inspected on the image Check the interface of the system. For example, after the image re-inspection system recognizes that the re-inspection result of the image of the PCB numbered PCB0001 in the database is defective, the image to be tested can be determined based on the parameter 3 Download001 of the image of the PCB numbered PCB0001 The storage path of the image is the folder Download001. Based on the parameter four PCB0001 of the image of the PCB numbered PCB0001, the image name of the image to be tested is determined as PCB0001.jpg, and the image in the folder Download001 is PCB0001.jpg. displayed in the system interface. For example, the interface can display the images to be tested for which the re-inspection result is flawed by page, the interface can display 5 images to be tested per page, a total of 20 pages, and the 20th page can display less than 5 of the images to be tested.

Step S5, the computer device distributes the image to be tested to a designated user through the image re-inspection system, and receives the marking of the image to be tested by the designated user, according to the marking of the image to be tested The final detection result of the image to be tested is fed back.

In one embodiment, the image re-inspection system may distribute the images to be tested whose re-inspection results are flaws to different designated users, and the designated users mark the images to be tested, The user is a staff member who has permission to log in to the image rechecking system to operate. For example, the image rechecking system can distribute the image to be tested to users corresponding to the type of the image to be tested according to the type of the image to be tested; it can also be distributed randomly; it can also be The distribution is performed according to the current number of images to be processed by the user (for example, the number of unmarked images to be tested) until all images to be tested are distributed.

In one embodiment, the final detection result of the image to be tested includes: the image to be tested is a flawless image, or the image to be tested is a flawed image.

In one embodiment, the designated user marks the image to be tested through different buttons or icons to determine the final detection result of the image to be tested. For example, the staff can log into the image re-inspection system, mark the images to be tested on the interface of the image re-inspection system, and there can be two options under each image to be tested. Buttons: "pass" and "ng", use the "pass" button to mark flawless images and the "ng" button to mark flawed images. When the worker determines that the image to be tested is a flawless image, he can click the "pass" button to mark it, or when the worker determines that the image to be tested is a flawed image, he can click "ng" button to mark it. It should be noted that the preset number of flawless PCB images obtained in step S2 are the images marked by the staff with the "pass" button obtained by the method described herein.

In one embodiment, the computer device may utilize the image review system to separately save images marked as "pass" and "ng".

In other embodiments, after all the images to be tested whose re-inspection results are flaws are marked by the staff, the computer device displays the images to be tested marked as "ng" on the images. In the interface of the re-inspection system, the parameters of the image to be tested are displayed below the image to be tested. For example, when the image of the PCB numbered PCB0001 is marked as "ng" by the staff, PCB0001.jpg can be displayed in the interface, and 09:30:05, 01, Download001, PCB0001 can be displayed below PCB0001.jpg, It is convenient for the staff to check and adjust the equipment in the No. 01 production line that produces PCB0001.

The above-mentioned FIG. 1 describes the image re-inspection method of the present application in detail. The following describes the hardware device architecture for implementing the image re-inspection method with reference to FIG. 2 .

It should be understood that the embodiments are only used for illustration, and are not limited by this structure in the scope of the patent application.

Referring to FIG. 2 , it is a schematic structural diagram of a computer device according to a preferred embodiment of the present application. In a preferred embodiment of the present application, the computer device 3 includes a storage 31 , at least one processor 32 , and a display 33 . 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 embodiments of the present application. More or less other hardware or software, or different component arrangements are shown.

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 design gate arrays, digital word processors and embedded devices, etc.

It should be noted that the computer device 3 is only an example, and other existing or future electronic products, if applicable to the present application, should also be included within the protection scope of the present application, and are incorporated herein by reference.

In some embodiments, the storage 31 is used for storing program codes and various data, and realizes high-speed and automatic access to programs or data during the operation of the computer device 3 . The storage 31 includes a read-only storage (Read-Only Memory, ROM), a programmable read-only storage (Programmable Read-Only Memory, PROM), an erasable programmable read-only storage (Erasable Programmable Read-only storage). -Only Memory, EPROM), One-time Programmable Read-Only Memory (OTPROM), Electronically-Erasable Programmable Read-Only Memory (EEPROM) , Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage, magnetic tape storage, or any other computer-readable storage medium that can be used to carry or store information.

In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, may be composed of a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions , including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital signal processing chips, graphics processors and combinations of various control chips, etc. 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 various components of the entire computer device 3 , and runs or executes the program code stored in the storage 31 . Or a module, and call the data stored in the storage 31 to execute various functions of the computer device 3 and process data, such as the function of image rechecking.

In some embodiments, referring to FIG. 1 , the storage 31 in the computer device 3 stores computer-readable instructions to implement an image rechecking method, and the processor 32 can execute the computer-readable instructions to implement The image rechecking method.

In some embodiments, the display 33 may be a display device capable of performing touch-screen operations and capable of presenting images. For example, the display 33 may be used to display the interface of the image rechecking system.

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 the power management device, so as to realize the realization through the power management device. Manage charging, discharging, and power management functions. The power supply may also include one or more of a DC or AC power source, a recharging device, a power failure detection circuit, a power converter or inverter, a power supply status indicator, or any other element. The computer device 3 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

It should be understood that the embodiments are only used for illustration, and are not limited by this structure in the scope of the patent application. In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and other division methods may be used in actual implementation.

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

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

It will be apparent to those skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, but that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Accordingly, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to fall within the scope of the claims. All changes within the meaning and scope of the equivalents of , are included in this application. Any reference sign in a claim should not be construed as limiting the claim to which it relates. Furthermore, it is clear that the word "comprising" does not exclude other units or, and the singular does not exclude the plural. Multiple units or means stated in the device claim may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names and do not denote any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limitations. Although the present application has been described in detail with reference to the above preferred embodiments, those of ordinary skill in the art should The technical solutions can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

3: Computer device 32: Processor 31: Storage 33: Display S1~S5: Steps

In order to more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only It is an embodiment of the present application. For those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without any creative effort.

FIG. 1 is a flowchart of an image rechecking method according to a preferred embodiment of the present application.

FIG. 2 is a structural diagram of a computer device according to a preferred embodiment of the present application.

S1~S5: Steps

Claims (10)

An image rechecking method, applied to a computer device, wherein the method comprises: obtaining an image to be tested and image information of the image to be tested, and the image to be tested is identified as a defective image in at least one detection; Use artificial intelligence programs to analyze the image information of the image to be tested to obtain the parameters of the image to be tested; Using the image re-inspection model obtained by pre-training, re-inspect the to-be-detected image based on the parameters of the to-be-detected image, and obtain the re-inspection result of the to-be-detected image; When the re-inspection result indicates that the image to be tested has defects, uploading the image to be tested to a pre-established image re-inspection system in the computer device; Distribute the image to be tested to a designated user through the image re-inspection system, receive the designated user's mark on the image to be measured, and give feedback to the image to be measured according to the mark of the image to be measured. The final test result of the test image. The image re-inspection method according to claim 1, wherein the image to be tested is an image of a printed circuit board (PCB), the method uses an automatic optical inspection AOI device to scan the PCB to obtain the image to be tested, and uses The AOI device saves the image to be measured in a preset path. The image re-inspection method according to claim 2, wherein the method further comprises obtaining the information of the PCB in the image to be tested. The image rechecking method according to claim 1, wherein the method further comprises: Obtain a preset number of flawless PCB images; image processing the predetermined number of flawless PCB images; and Using the processed unblemished PCB image to train a neural network to obtain the image rechecking model. The image rechecking method according to claim 1, wherein the method further comprises: Using the artificial intelligence program, the parameters of the image to be tested are input into the image rechecking model. The image rechecking method according to claim 1, wherein the method further comprises: Based on the parameters of the image to be tested, the image to be tested is obtained by using the image rechecking model. The image rechecking method according to claim 1, wherein the method further comprises: inputting the parameters of the image to be tested and the re-inspection results into the artificial intelligence program using the image re-inspection model; and Using the artificial intelligence program, the parameters of the image to be tested and the rechecking result are stored in a pre-established database. The image re-inspection method according to claim 1, wherein the final detection result of the image to be tested includes: the image to be tested is a flawless image, or the image to be tested is a flawed 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, realizes the image rechecking according to any one of claim items 1 to 8 method. A computer device, wherein the computer device includes a memory and at least one processor, the memory stores at least one instruction, and when the at least one instruction is executed by the at least one processor, implements as claimed in items 1 to 8 The image rechecking method of any one of the above.

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