1. A method of workpiece defect detection, the method comprising:

receiving a workpiece detection signal, and acquiring an optimal exposure parameter of a detected workpiece corresponding to the workpiece detection signal;

adjusting the actual exposure parameter of image acquisition equipment to be the optimal exposure parameter, and controlling the image acquisition equipment to acquire an image of the detection workpiece to obtain an image of the detection workpiece;

and carrying out defect detection operation on the detected workpiece image.

2. The method of claim 1, wherein said step of obtaining optimal exposure parameters for detecting the workpiece corresponding to said workpiece detection signals comprises:

acquiring the type of the detection workpiece;

and matching the optimal exposure parameters corresponding to the type of the detected workpiece.

3. The method of claim 1, wherein said step of receiving a workpiece inspection signal and obtaining optimal exposure parameters for inspecting the workpiece corresponding to said workpiece inspection signal comprises:

acquiring exposure images of the detection workpiece under a plurality of preset exposure parameters and a trained exposure recognition model;

sequentially taking the exposure images as the input of the trained exposure recognition model, and operating the trained exposure recognition model;

and determining the optimal exposure parameter of the type corresponding to the detected workpiece according to the first output result of the trained exposure identification model.

4. The method of claim 3, wherein the step of obtaining exposure images of the inspected workpiece at a plurality of predetermined exposure parameters is preceded by the step of:

acquiring a plurality of groups of workpiece exposure data, wherein each group of workpiece exposure data comprises exposure images of a workpiece under a plurality of preset exposure parameters and an exposure detection result corresponding to each exposure image;

and training the initial exposure recognition model through the multiple groups of workpiece exposure data to obtain a trained exposure recognition model.

5. The method of claim 1, wherein said step of performing a defect detection operation on said inspected workpiece image comprises:

acquiring a trained defect identification model;

taking the detected workpiece image as the input of the trained defect recognition model, and operating the trained defect recognition model;

and determining the defect type of the detected workpiece according to the second output result of the trained defect identification model.

6. The method of claim 5, wherein the step of determining the type of flaw in the inspected workpiece based on the second output of the trained flaw recognition model comprises:

acquiring the defect size and the defect position in the second output result;

and determining the defect type of the detected workpiece according to the defect size and the defect position.

7. The method of claim 6, wherein said step of determining a defect type of said inspected workpiece based on said defect size and said defect location comprises:

determining the defect degree of the detected workpiece according to the defect size;

and determining the defect type of the detected workpiece according to the defect position.

8. An electronic device, comprising:

the first acquisition module is used for receiving a workpiece detection signal and acquiring the optimal exposure parameter of a detected workpiece corresponding to the workpiece detection signal;

the first execution module is used for adjusting the exposure parameter of the image acquisition equipment to be the optimal exposure parameter and controlling the image acquisition equipment to acquire the image of the detection workpiece to obtain an image of the detection workpiece;

and the second execution module is used for carrying out defect detection operation on the detected workpiece image.

9. A workpiece defect detection apparatus, characterized in that the workpiece defect detection apparatus comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the workpiece defect detection method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the workpiece defect detection method according to any one of claims 1 to 7.

Background

During the production process of a workpiece, due to casting materials, temperature, equipment extrusion, friction or collision in the transmission process and the like, the surface of the workpiece has the defects of sand holes, scratches, bruises and the like; in the prior art, in order to detect the defects of a workpiece, the image of the workpiece is mostly subjected to characteristic analysis to judge whether the workpiece has defects; however, due to the difference of the structural forms of the workpieces, the quality of images generated by different workpieces under the same exposure is different, and thus the accuracy of defect judgment is affected.

Disclosure of Invention

The invention mainly aims to provide a workpiece defect detection method, an electronic device, a device and a readable storage medium, and aims to solve the problem that the structural form of a workpiece in the prior art is not high in accuracy under the same exposure.

In order to achieve the above object, the present invention provides a method for detecting defects of a workpiece, the method comprising the steps of:

receiving a workpiece detection signal, and acquiring an optimal exposure parameter of a detected workpiece corresponding to the workpiece detection signal;

adjusting the actual exposure parameter of image acquisition equipment to be the optimal exposure parameter, and controlling the image acquisition equipment to acquire an image of the detection workpiece to obtain an image of the detection workpiece;

and carrying out defect detection operation on the detected workpiece image.

Optionally, the step of acquiring an optimal exposure parameter of the detected workpiece corresponding to the workpiece detection signal includes:

acquiring the type of the detection workpiece;

and matching the optimal exposure parameters corresponding to the type of the detected workpiece.

Optionally, the step of receiving a workpiece detection signal and acquiring an optimal exposure parameter of a detected workpiece corresponding to the workpiece detection signal includes:

acquiring exposure images of the detection workpiece under a plurality of preset exposure parameters and a trained exposure recognition model;

sequentially taking the exposure images as the input of the trained exposure recognition model, and operating the trained exposure recognition model;

and determining the optimal exposure parameter of the type corresponding to the detected workpiece according to the first output result of the trained exposure identification model.

Optionally, the step of acquiring exposure images of the detected workpiece under a plurality of preset exposure parameters includes:

acquiring a plurality of groups of workpiece exposure data, wherein each group of workpiece exposure data comprises exposure images of a workpiece under a plurality of preset exposure parameters and an exposure detection result corresponding to each exposure image;

and training the initial exposure recognition model through the multiple groups of workpiece exposure data to obtain a trained exposure recognition model.

Optionally, the step of performing a defect detection operation on the detected workpiece image includes:

acquiring a trained defect identification model;

taking the detected workpiece image as the input of the trained defect recognition model, and operating the trained defect recognition model;

and determining the defect type of the detected workpiece according to the second output result of the trained defect identification model.

Optionally, the step of determining the defect type of the detected workpiece according to the second output result of the trained defect recognition model includes:

acquiring the defect size and the defect position in the second output result;

and determining the defect type of the detected workpiece according to the defect size and the defect position.

Optionally, the step of determining the defect type of the detected workpiece according to the defect size and the defect position includes:

determining the defect degree of the detected workpiece according to the defect size;

and determining the defect type of the detected workpiece according to the defect position.

In order to achieve the above object, the present invention also provides an electronic device, including:

the first acquisition module is used for receiving a workpiece detection signal and acquiring the optimal exposure parameter of a detected workpiece corresponding to the workpiece detection signal;

a first execution module for adjusting the exposure parameter of the image acquisition equipment to the optimal exposure parameter

Controlling the image acquisition equipment to acquire images of the detection workpiece to obtain an image of the detection workpiece;

and the second execution module is used for carrying out defect detection operation on the detected workpiece image.

Optionally, the first obtaining module includes:

the first acquisition submodule is used for acquiring the type of the detection workpiece;

and the first execution sub-module is used for matching the optimal exposure parameter corresponding to the type of the detected workpiece.

Optionally, the electronic device further comprises:

the second acquisition module is used for acquiring exposure images of the detection workpiece under a plurality of preset exposure parameters and a trained exposure recognition model;

the third execution module is used for sequentially taking the exposure images as the input of the trained exposure recognition model and operating the trained exposure recognition model;

and the fourth execution module is used for determining the optimal exposure parameter of the type corresponding to the detected workpiece according to the first output result of the trained exposure identification model.

Optionally, the electronic device further comprises:

the third acquisition module is used for acquiring a plurality of groups of workpiece exposure data, and each group of workpiece exposure data comprises exposure images of the workpiece under a plurality of preset exposure parameters and an exposure detection result corresponding to each exposure image;

and the fifth execution module is used for training the initial exposure recognition model through the multiple groups of workpiece exposure data to obtain a trained exposure recognition model.

Optionally, the second execution module includes:

the second acquisition submodule is used for acquiring the trained defect identification model;

the second execution submodule is used for taking the detected workpiece image as the input of the trained defect recognition model and operating the trained defect recognition model;

and the third execution submodule is used for determining the defect type of the detected workpiece according to the second output result of the trained defect identification model.

Optionally, the third execution submodule includes:

the first acquisition unit is used for acquiring the defect size and the defect position in the second output result;

and the first execution unit is used for determining the defect type of the detected workpiece according to the defect size and the defect position.

Optionally, the first execution unit includes:

the first execution subunit is used for determining the defect degree of the detected workpiece according to the defect size;

and the second execution subunit is used for determining the defect type of the detected workpiece according to the defect position.

To achieve the above object, the present invention further provides a workpiece defect detecting apparatus, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the workpiece defect detecting method as described above.

To achieve the above object, the present invention further provides a computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the workpiece defect detection method as described above.

The invention provides a workpiece defect detection method, an electronic device, a device and a readable storage medium, which receive a workpiece detection signal and acquire an optimal exposure parameter of a detected workpiece corresponding to the workpiece detection signal; adjusting the actual exposure parameter of image acquisition equipment to be the optimal exposure parameter, and controlling the image acquisition equipment to acquire an image of the detection workpiece to obtain an image of the detection workpiece; and carrying out defect detection operation on the detected workpiece image. By acquiring the optimal exposure parameters of the workpiece and acquiring the image of the detected workpiece based on the optimal exposure parameters of the workpiece, the image of the workpiece for defect detection can restore the details of the workpiece to the maximum extent, and the influence of the exposure parameters on the accuracy of the defect detection of the workpiece is avoided.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.

FIG. 1 is a schematic flow chart illustrating a method for detecting defects on a workpiece according to a first embodiment of the present invention;

FIG. 2 is a detailed flowchart of step S30 of the fifth embodiment of the method for detecting defects of workpieces according to the present invention;

FIG. 3 is a schematic block diagram of a workpiece defect inspection apparatus according to the present invention.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The invention provides a workpiece defect detection method, referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of the workpiece defect detection method of the invention, and the method comprises the following steps:

step S10, receiving a workpiece detection signal, and acquiring an optimal exposure parameter of a detected workpiece corresponding to the workpiece detection signal;

the workpiece detection signal is a signal for triggering a workpiece defect detection process; the workpiece detection signal can be sent by a detection person, and can be automatically triggered by setting a trigger device, if the prepared workpiece is conveyed to a defect detection station through a transmission device, an induction device such as a limit switch, a sensor or a camera is arranged on the defect detection station, and when the induction device detects that the workpiece reaches the defect detection station, the workpiece detection signal is triggered to take effect.

The detection workpiece corresponding to the workpiece detection signal can be confirmed by a detection workpiece identifier which is sent by a detection person when the detection person sends the workpiece detection signal, and the type of the detection workpiece can be confirmed by acquiring an image of the detection workpiece and further identifying the image through a recognition algorithm, a model and the like. The optimal exposure parameters and the detection workpieces are correspondingly set, it can be understood that the corresponding relationship between the optimal exposure parameters and the detection workpieces can be preset in advance, and can also be detected in real time, and whether the preset or the real-time detection is adopted in advance can be set according to actual application scenes and needs, which is not described herein.

Step S20, adjusting the actual exposure parameter of the image acquisition equipment to the optimal exposure parameter, and controlling the image acquisition equipment to acquire the image of the detection workpiece to obtain the image of the detection workpiece;

after the optimal exposure parameters corresponding to the detected workpiece are obtained, the image of the detected workpiece is obtained under the optimal exposure parameters, so that the image of the detected workpiece with the optimal imaging effect is obtained. The image acquisition device in this embodiment is a camera.

And step S30, performing defect detection operation on the detected workpiece image.

The defect detection operation is an operation of detecting defects of the workpiece; the defect detection operation can be performed through a deep learning model, an identification algorithm or a manual mode, and can be selected according to actual application scenes and needs, which are not repeated herein.

According to the embodiment, the optimal exposure parameters of the workpiece are obtained, and the image of the detected workpiece is obtained based on the optimal exposure parameters of the workpiece, so that the image of the workpiece for defect detection can restore the details of the workpiece to the maximum extent, and the influence of the exposure parameters on the accuracy of the defect detection of the workpiece is avoided.

Further, in the second embodiment of the workpiece defect detecting method of the present invention proposed based on the first embodiment of the present invention, the step S10 includes the steps of:

step S11, acquiring the type of the detected workpiece;

and step S12, matching the optimal exposure parameters corresponding to the type of the detected workpiece.

The structures of the detection workpieces of the same type are generally similar, so that the detection workpieces of the same type can correspond to the same optimal exposure parameters; the type of workpiece detected includes, but is not limited to, model, category, etc. It should be noted that the detected workpiece may include a plurality of type attributes, and when different types included in the detected workpiece correspond to different optimal exposure parameters, the optimal exposure parameter corresponding to the lowest type is used as the optimal exposure parameter of the detected workpiece.

Further, the optimal exposure parameters corresponding to the detected workpieces can also be directly obtained, for example, an exposure parameter correspondence table corresponding to the optimal exposure parameters corresponding to each detected workpiece is preset, and the optimal exposure parameters corresponding to the detected workpieces are directly matched in the exposure parameter correspondence table; or detecting the optimal exposure parameters of the detected workpiece through the trained exposure recognition model; specifically, exposure images of a detected workpiece under a plurality of preset exposure parameters are respectively obtained, the exposure images are sequentially used as input of a trained exposure recognition model, the trained exposure recognition model is operated, and the optimal exposure parameter of the detected workpiece is determined according to an output result of the trained exposure recognition model.

The embodiment can reasonably acquire the optimal exposure parameters of the detected workpiece.

Further, in a third embodiment of the method for detecting defects of a workpiece according to the present invention proposed based on the first embodiment of the present invention, the method includes, before the step S10, the steps of:

step S40, acquiring exposure images of the detection workpiece under a plurality of preset exposure parameters and a trained exposure recognition model;

step S50, the exposure images are sequentially used as the input of the trained exposure recognition model, and the trained exposure recognition model is operated;

and step S60, determining the optimal exposure parameter of the type corresponding to the detected workpiece according to the first output result of the trained exposure recognition model.

The preset exposure parameters in this embodiment include a low exposure parameter with an exposure time of 8000ms, a medium exposure parameter with an exposure time of 14000ms, and a high exposure parameter with an exposure time of 20000 ms; it can be understood that the type, number, and specific numerical value of the specific preset exposure parameter may be set according to the actual application scenario and the need, which is not described herein again.

The first output result comprises a prediction result corresponding to each exposure image, the prediction result specifically comprises the probability that the exposure parameter of the exposure image is the optimal exposure parameter, and the exposure parameter corresponding to the exposure image with the highest probability that the exposure parameter is the optimal exposure parameter is used as the optimal exposure parameter for detecting the type corresponding to the workpiece.

It should be noted that, for determining the optimal exposure parameters of the same type of workpieces, it is necessary to obtain exposure images of the plurality of workpieces in the type under the plurality of preset exposure parameters, respectively identify the exposure images of the plurality of workpieces under the plurality of preset exposure parameters through the trained exposure recognition model, and comprehensively determine the optimal exposure parameters of the type according to all the first output results output by the trained exposure recognition model.

According to the embodiment, the optimal exposure parameters corresponding to the types of the detected workpieces can be obtained through the trained exposure recognition model.

Further, in a fourth embodiment of the method for detecting defects of a workpiece according to the present invention based on the third embodiment of the present invention, the step S40 is preceded by the steps of:

step S70, acquiring a plurality of groups of workpiece exposure data, wherein each group of workpiece exposure data comprises exposure images of the workpiece under a plurality of preset exposure parameters and an exposure detection result corresponding to each exposure image;

and step S80, training the initial exposure recognition model through the multiple groups of workpiece exposure data to obtain a trained exposure recognition model.

The method comprises the following steps that detection personnel set a corresponding label for each exposure image of a workpiece in advance, namely an exposure detection result; workpiece exposure data can be divided into a training set, a verification set and a test set; the training set is used to estimate the model, the validation set is used to determine the network structure or parameters that control the complexity of the model, and the test set examines how well the model is performing to the final choice of the best model. Training an initial exposure recognition model through the exposure image and the corresponding label; it can be understood that the conventional settings such as the loss function and the training end condition in the training process of the initial exposure recognition model can be selected according to the actual application scenario and the requirement, and are not described herein again. And taking the initial exposure recognition model reaching the training end condition as the exposure recognition model after the training is finished.

The embodiment can reasonably train the initial exposure recognition model to obtain the trained exposure recognition model.

Further, referring to fig. 2, in a fifth embodiment of the workpiece defect detecting method of the present invention proposed based on the first embodiment of the present invention, the step S30 includes the steps of:

step S31, acquiring a defect recognition model after training;

step S32, using the detected workpiece image as the input of the trained defect recognition model, and operating the trained defect recognition model;

and step S33, determining the defect type of the detected workpiece according to the second output result of the trained defect recognition model.

The step S33 includes the steps of:

step S331, acquiring the defect size and the defect position in the second output result;

step S332, determining the defect type of the detected workpiece according to the defect size and the defect position.

Further, the step S332 includes the steps of:

step S3321, determining the defect degree of the detected workpiece according to the defect size;

step S3322, determining the defect type of the detected workpiece according to the defect position.

The training mode of the defect recognition model is similar to that of the exposure recognition model, and can be specifically selected according to actual application scenes and needs, which are not repeated herein.

Defect size includes, but is not limited to, dimensions such as area, length, width, and contour length of the defect; the defect size is used for representing the severity of the defect; the detected defects can be classified according to the defect size, and in the embodiment, the defects are classified into three types, namely first-level defects, second-level defects and third-level defects according to the defect degree; setting numerical value ranges of the area, the length, the width and the outline length of the defect corresponding to the defect grade respectively; if the area is smaller than the first area threshold, the first-level defect is located, and if the area is larger than the first area threshold and smaller than the second area threshold, the second-level defect is located, wherein the first area threshold is smaller than the second area threshold, and if the area is larger than the second area threshold, the third-level defect is located; the length, width and contour length of the defect can be set according to the area; the first-level defects in the embodiment indicate that the detected workpiece has no defects or the defects are within a negligible range, the second-level defects indicate that the defects of the detected workpiece need to be repaired, and the third-level defects indicate that the defects of the detected workpiece cannot be repaired. It should be noted that the number of levels of the defect degree may be set according to the actual application scenario and the need, which is not described herein.

Judging the defect degree of a plurality of dimensions of the defect size, and taking the most serious defect degree as the defect degree of the detected workpiece; and if the area of the defect is judged to be a first-level defect, the length is a second-level defect, the width is a first-level defect, and the outline length is a first-level defect, the most serious defect degree, namely the second-level defect is taken as the defect degree of the detected workpiece.

Because the defect types of the same type of detection workpieces appearing at the same position have similarity, the defect types of the detection workpieces can be judged in an auxiliary manner through the defect positions.

The defect type and the defect degree of the detected workpiece can be accurately determined.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.

The present application further provides an electronic device for implementing the method for detecting defects of a workpiece, the electronic device comprising:

the first acquisition module is used for receiving a workpiece detection signal and acquiring the optimal exposure parameter of a detected workpiece corresponding to the workpiece detection signal;

the first execution module is used for adjusting the exposure parameter of the image acquisition equipment to be the optimal exposure parameter;

the second acquisition module is used for controlling the image acquisition equipment to acquire images of the detection workpieces to obtain images of the detection workpieces;

and the second execution module is used for carrying out defect detection operation on the detected workpiece image.

It should be noted that the first obtaining module in this embodiment may be configured to execute step S10 in this embodiment, the first executing module in this embodiment may be configured to execute step S20 in this embodiment, and the second executing module in this embodiment may be configured to execute step S30 in this embodiment.

By acquiring the optimal exposure parameters of the workpiece and acquiring the image of the detected workpiece based on the optimal exposure parameters of the workpiece, the image of the workpiece for defect detection can restore the details of the workpiece to the maximum extent, and the influence of the exposure parameters on the accuracy of the defect detection of the workpiece is avoided.

Further, the first obtaining module comprises:

the first acquisition submodule is used for acquiring the type of the detection workpiece;

and the first execution sub-module is used for matching the optimal exposure parameter corresponding to the type of the detected workpiece.

Further, the electronic device further includes:

the second acquisition module is used for acquiring exposure images of the detection workpiece under a plurality of preset exposure parameters and a trained exposure recognition model;

the third execution module is used for sequentially taking the exposure images as the input of the trained exposure recognition model and operating the trained exposure recognition model;

and the fourth execution module is used for determining the optimal exposure parameter of the type corresponding to the detected workpiece according to the first output result of the trained exposure identification model.

Further, the electronic device further includes:

the third acquisition module is used for acquiring a plurality of groups of workpiece exposure data, and each group of workpiece exposure data comprises exposure images of the workpiece under a plurality of preset exposure parameters and an exposure detection result corresponding to each exposure image;

and the fifth execution module is used for training the initial exposure recognition model through the multiple groups of workpiece exposure data to obtain a trained exposure recognition model.

Further, the second execution module includes:

the second acquisition submodule is used for acquiring the trained defect identification model;

the second execution submodule is used for taking the detected workpiece image as the input of the trained defect recognition model and operating the trained defect recognition model;

and the third execution submodule is used for determining the defect type of the detected workpiece according to the second output result of the trained defect identification model.

Further, the third execution submodule includes:

the first acquisition unit is used for acquiring the defect size and the defect position in the second output result;

and the first execution unit is used for determining the defect type of the detected workpiece according to the defect size and the defect position.

Further, the first execution unit includes:

the first execution subunit is used for determining the defect degree of the detected workpiece according to the defect size;

and the second execution subunit is used for determining the defect type of the detected workpiece according to the defect position.

It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. The modules may be implemented by software as part of the apparatus, or may be implemented by hardware, where the hardware environment includes a network environment.

Referring to fig. 3, the workpiece defect detecting apparatus may include components such as a communication module 10, a memory 20, and a processor 30 in a hardware configuration. In the workpiece defect detecting apparatus, the processor 30 is connected to the memory 20 and the communication module 10, respectively, the memory 20 stores thereon a computer program, and the computer program is executed by the processor 30 at the same time, and when the computer program is executed, the steps of the above-mentioned method embodiment are realized.

The communication module 10 may be connected to an external communication device through a network. The communication module 10 may receive a request from an external communication device, and may also send a request, an instruction, and information to the external communication device, where the external communication device may be another workpiece defect detection apparatus, a server, or an internet of things device, such as a television.

The memory 20 may be used to store software programs as well as various data. The memory 20 may mainly include a program storage area and a data storage area, where the program storage area may store an operating system, an application program required by at least one function (for example, controlling the image capturing device to capture an image of the detected workpiece to obtain an image of the detected workpiece), and the like; the storage data area may include a database, and the storage data area may store data or information created according to use of the system, or the like. Further, the memory 20 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

The processor 30, which is a control center of the workpiece defect inspection apparatus, connects various parts of the entire workpiece defect inspection apparatus using various interfaces and lines, and performs various functions of the workpiece defect inspection apparatus and processes data by running or executing software programs and/or modules stored in the memory 20 and calling data stored in the memory 20, thereby integrally monitoring the workpiece defect inspection apparatus. Processor 30 may include one or more processing units; alternatively, the processor 30 may integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 30.

Although not shown in fig. 3, the workpiece defect detecting apparatus may further include a circuit control module, which is connected to a power supply to ensure the normal operation of other components. Those skilled in the art will appreciate that the workpiece defect inspection apparatus configuration shown in FIG. 3 does not constitute a limitation of the workpiece defect inspection apparatus and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.

The invention also proposes a computer-readable storage medium on which a computer program is stored. The computer-readable storage medium may be the Memory 20 in the workpiece defect detecting apparatus in fig. 3, and may also be at least one of a ROM (Read-Only Memory)/RAM (Random Access Memory), a magnetic disk, and an optical disk, where the computer-readable storage medium includes instructions for enabling a terminal device (which may be a television, an automobile, a mobile phone, a computer, a server, a terminal, or a network device) having a processor to execute the method according to the embodiments of the present invention.

In the present invention, the terms "first", "second", "third", "fourth" and "fifth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance, and those skilled in the art can understand the specific meanings of the above terms in the present invention according to specific situations.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Although the embodiment of the present invention has been shown and described, the scope of the present invention is not limited thereto, it should be understood that the above embodiment is illustrative and not to be construed as limiting the present invention, and that those skilled in the art can make changes, modifications and substitutions to the above embodiment within the scope of the present invention, and that these changes, modifications and substitutions should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.