Metal carbide grading method, device and equipment and storage medium

Abstract

The invention is suitable for the technical field of electrical digital data processing, and provides a metal carbide grading method, device and equipment and a storage medium, and the method comprises the following steps: obtaining a fluorescence microscopic image and a metallographic microscopic image of a target object; carrying out image superposition on the fluorescence microscopic image and the metallographic microscopic image to obtain a first image; and marking the distribution and the size of the carbide in the first image, and inputting the marked first image into a carbide grading model to obtain a grading result of the target object. The method can improve the classification accuracy of target object carbides.

Description

Technical Field

[0001] The present application belongs to the technical field of electrical digital data processing, and in particular to a metal carbide grading method, device, equipment and storage medium. Background Art

[0002] During the steel production process, different grades of metal carbides will produce various variations in shape and structure. Data identification of metal carbides is of great significance to ensuring metallurgical quality. For example, the unevenness of metal carbides is an important indicator for measuring metallurgical quality.

[0003] In the related art, the detection method for metal carbides is to use a metal carbide grading model to grade metal carbides. Specifically, each image to be detected of the sample to be detected is input into a preliminary screening model to obtain a multi-channel feature map of each image to be detected, and the metal carbide image in the image to be detected is determined according to each multi-channel feature map, so that the metal carbide image is sequentially input into a target detection model, a grading model and an integrated model to obtain the final carbide grade of the sample to be detected.

[0004] In the above-mentioned methods, the image processed by the initial screening model will have a large error and cannot clearly and accurately display information such as the morphology and distribution of carbides, resulting in a low accuracy of the final carbide classification. Summary of the invention

[0005] The embodiments of the present application provide a metal carbide grading method, device, equipment and storage medium to solve the technical problem that the accuracy of the final carbide grading is low because the image processed by the initial screening model cannot clearly and accurately display the morphology and distribution of the carbides.

[0006] This application is implemented through the following technical solutions: In a first aspect, an embodiment of the present application provides a metal carbide classification method, comprising: Acquire fluorescence microscopy images and metallographic microscopy images of target objects.

[0007] The fluorescence microscopic image and the metallographic microscopic image are superimposed to obtain a first image.

[0008] The distribution and size of the carbides in the first image are marked, and the marked first image is input into a carbide classification model to obtain a classification result of the target object.

[0009] In combination with the first aspect, in some possible implementations, superimposing the fluorescence microscopic image and the metallographic microscopic image to obtain a first image includes: The fluorescence microscopic image is registered according to the scale and size of the metallographic microscopic image to obtain a second image.

[0010] The second image and the metallographic microscope image are superimposed and fused to obtain the first image.

[0011] In combination with the first aspect, in some possible implementations, the carbide classification model is a neural network model.

[0012] The neural network model uses a Canny edge detection algorithm or a Sobel operator detection algorithm to determine the distribution area and size of the carbides according to the marks in the first image.

[0013] Based on the distribution area and size of the carbides, the first image is classified to determine a classification result of the first image.

[0014] In combination with the first aspect, in some possible implementations, determining the distribution area and size of carbides according to the marks in the first image includes: Based on the markings in the first image, noisy distribution areas of carbides are determined.

[0015] The noise points in the noisy distribution area are removed to obtain the distribution area of ​​carbides.

[0016] Based on the distribution area of ​​carbides, the size of the carbides is determined.

[0017] In conjunction with the first aspect, in some possible implementations, obtaining a fluorescence microscopic image of a target object includes: The target object is corroded by nitric acid alcohol corrosive solution.

[0018] The etched target object is sequentially subjected to ultrasonic cleaning, plasma treatment and immersion in a silane coupling agent.

[0019] Cures the silane coupling agent on the surface of the target object.

[0020] The target object that has completed the curing operation is fluorescently marked with a fluorescent agent, and the unreacted fluorescent agent is washed away after the marking is completed.

[0021] The fluorescently labeled target object is observed through a fluorescence microscope to obtain a fluorescence microscopic image.

[0022] In combination with the first aspect, in some possible implementations, the silane coupling agent is a mixture of a phosphate buffer solution and an ethanol solution of 3-aminopropyltriethoxysilane.

[0023] The fluorescent agent is fluorescein isothiocyanate.

[0024] In conjunction with the first aspect, in some possible implementations, obtaining a metallographic microscopic image of a target object includes: The target object after being corroded by nitric acid alcohol corrosive solution is observed through a metallographic microscope to obtain a metallographic microscopic image.

[0025] In a second aspect, an embodiment of the present application provides a metal carbide classification device, comprising: The data acquisition module is used to acquire the fluorescence microscopic image and metallographic microscopic image of the target object.

[0026] The image overlay module is used to overlay the fluorescence microscopic image and the metallographic microscopic image to obtain a first image.

[0027] The result output module is used to mark the distribution and size of the carbides in the first image, and input the marked first image into the carbide classification model to obtain the classification result of the target object.

[0028] In a third aspect, an embodiment of the present application provides a terminal device, comprising: a processor and a memory, the memory being used to store a computer program, and the processor implementing the metal carbide grading method as described in any one of the first aspects when executing the computer program.

[0029] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the metal carbide grading method as described in any one of the first aspects is implemented.

[0030] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0031] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present application first captures a fluorescent microscopic image and a metallographic microscopic image of the target object. Subsequently, the two images are superimposed to generate a first image. Next, the first image is marked and input into a carbide grading model to finally obtain the carbide grading result of the target object. This solution omits the processing step of the preliminary screening model, effectively avoiding the image error introduced by the preliminary screening model processing, and can clearly and accurately display key information such as the morphology and distribution of carbides, thereby improving the accuracy of the final carbide grading result.

[0032] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0034] Figure 1 It is a schematic diagram of a process of a metal carbide classification method provided in one embodiment of the present application; Figure 2 is a schematic diagram of image registration and superposition fusion provided by an embodiment of the present application; Figure 3 is a time sequence flow chart of a metal carbide classification method provided in one embodiment of the present application; Figure 4 It is a structural schematic diagram of a metal carbide classification device provided in one embodiment of the present application; Figure 5 It is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0036] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0037] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0038] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0039] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0040] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0041] The present application embodiment provides a metal carbide classification method, Figure 1 is a schematic diagram of the process of the metal carbide classification method provided in one embodiment of the present application, referring to Figure 1 , the metal carbide classification method is described in detail as follows: Step 101, obtaining a fluorescence microscopic image and a metallographic microscopic image of a target object.

[0042] Exemplarily, obtaining a fluorescence microscopic image of a target object includes: The target object is corroded by nitric acid alcohol corrosive solution.

[0043] The etched target object is sequentially subjected to ultrasonic cleaning, plasma treatment and immersion in a silane coupling agent.

[0044] Cures the silane coupling agent on the surface of the target object.

[0045] The target object that has completed the curing operation is fluorescently marked with a fluorescent agent, and the unreacted fluorescent agent is washed away after the marking is completed.

[0046] The fluorescently labeled target object is observed through a fluorescence microscope to obtain a fluorescence microscopic image.

[0047] In combination with the first aspect, in some possible implementations, the silane coupling agent is a mixture of a phosphate buffer solution and an ethanol solution of 3-aminopropyltriethoxysilane.

[0048] The fluorescent agent is fluorescein isothiocyanate.

[0049] Exemplarily, the nitric acid alcohol etching solution may be a 4% nitric acid alcohol etching solution, and the etching time is 10 seconds to 60 seconds.

[0050] By way of example, the specific process of “subjecting the corroded target object to ultrasonic cleaning, plasma treatment and immersion in a silane coupling agent in sequence” may include the following processes: The sample after etching was placed in a beaker containing anhydrous ethanol and ultrasonically cleaned to remove impurities. After cleaning, the sample was dried and plasma treated to achieve surface modification. By treating the sample with oxygen plasma for 10 to 30 minutes, the polarity and hydrophilicity of the sample surface can be effectively improved (M+O→MOM+H2O→M(OH)2, where M represents the atoms on the surface of the metal substrate), thereby promoting the subsequent attachment of 3-aminopropyltriethoxysilane (APTES). Add phosphate buffer (PBS) to the APTES solution, measure the pH value with a pH meter, add HCl or NaOH, stir well and re-measure the pH value until the desired pH range of 7±0.5 is reached. Avoid adding too much acid or base at one time to avoid over-adjustment. A pH value of 7±0.5 can more effectively introduce amino groups on the surface of the metal substrate while minimizing the impact on carbides. The treated sample was immersed in an ethanol solution of the silane coupling agent APTES with a pH adjusted to introduce amino groups for surface functionalization. APTES can introduce abundant amino groups on the surface of metal substrates (hydrolysis reaction: (CH3CH2O)3Si-CH2CH2CH2NH2+3H2O→Si(OH)3-CH2CH2CH2NH2+3C2H5OH; reaction of silanol groups with hydroxyl groups on metal surface: Si(OH)3-CH2CH2CH2NH2+M-OH→Si-O-M+H2O; condensation of remaining silanol groups: 2Si(OH)3-CH2CH2CH2NH2→Si-O-Si-CH2CH2CH2NH2-2H2O), while on the surface of carbides, this reaction either does not occur or the reaction degree is low. Finally, X-ray photoelectron spectroscopy (XPS) technology was used to confirm whether amino groups were successfully introduced on the sample surface.

[0051] To illustrate, the specific process of "curing the silane coupling agent on the surface of the target object" includes the following steps: First, heat the beaker to 25°C and keep it for 20 minutes. Then, take out the sample, wash it several times with anhydrous ethanol, and dry it in a drying oven at 80°C for 30 minutes to ensure that the APTES is completely cured.

[0052] For example, the fluorescent agent must be activated before use, and the specific steps of the activation process are as follows: S1: Preparation of activated fluorescent agent solution: First, prepare 5 mM 1-(3-methylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) and 10 mM N-hydroxysuccinimide (NHS) solution; S2: Dissolve the fluorescent agent: Then dissolve fluorescein isothiocyanate (FITC) in dimethyl sulfoxide (DMSO) or dimethylformamide (DMF); S3: Perform fluorescent agent activation: add FITC solution to the prepared EDC and NHS solution, ensuring that the ratio of FITC to EDC / NHS is 1:10, heat to 25°C in a beaker and mix by ultrasonic stirring for 20 minutes to promote the reaction; S4: Termination of the reaction: The reaction is stopped by adding an excess of the amino compound glycine, and the reaction product is then transferred to a dialysis bag and dialyzed with a buffer solution to remove small molecule impurities. The activated fluorescein enables the isothiocyanate group in FITC to form a covalent bond with the amino group on the surface of the functionalized metal substrate.

[0053] To illustrate, the detailed steps of "fluorescently marking an object that has completed the curing process by using a fluorescent agent, and washing away the unreacted fluorescent agent after marking" are as follows: First, fluorescein isothiocyanate (FITC) is used as a marking agent. Next, the functionalized sample is placed in an activated FITC solution and ultrasonically stirred in a beaker at 25°C for 1 hour to complete the marking. After marking, the sample is taken out and the surface is cleaned with anhydrous ethanol to remove the unreacted fluorescent agent. Finally, the sample is placed in a drying oven and dried at 25°C.

[0054] Exemplarily, obtaining a metallographic microscopic image of a target object includes: The target object after being corroded by nitric acid alcohol corrosive solution is observed through a metallographic microscope to obtain a metallographic microscopic image.

[0055] Step 102: Superimpose the fluorescence microscopic image and the metallographic microscopic image to obtain a first image.

[0056] Exemplarily, step 102 may include: The fluorescence microscopic image is registered according to the scale and size of the metallographic microscopic image to obtain a second image.

[0057] The second image and the metallographic microscope image are superimposed and fused to obtain the first image.

[0058] For example, Figure 2 As shown, in order to ensure that the distribution range of the same metal carbide does not change, it is necessary to align the size and proportion of the fluorescence microscopic image and the metallographic microscopic image to ensure the accuracy of the superimposed image, that is, the first image.

[0059] Step 103 , marking the distribution and size of the carbides in the first image, and inputting the marked first image into a carbide classification model to obtain a classification result of the target object.

[0060] Exemplarily, the carbide classification model may be a neural network model.

[0061] The neural network model uses a Canny edge detection algorithm or a Sobel operator detection algorithm to determine the distribution area and size of the carbides according to the marks in the first image.

[0062] Based on the distribution area and size of the carbides, the first image is classified to determine a classification result of the first image.

[0063] Exemplarily, determining the distribution area and size of carbides according to the marks in the first image may include: Based on the markings in the first image, noisy distribution areas of carbides are determined.

[0064] The noise points in the noisy distribution area are removed to obtain the distribution area of ​​carbides.

[0065] Based on the distribution area of ​​carbides, the size of the carbides is determined.

[0066] Exemplarily, in order to ensure the accuracy of the first image classification result, it is necessary to remove noise points in the first image, eliminate the influence of noise, improve accuracy, and ultimately improve the accuracy of metal carbide classification.

[0067] For example, to facilitate understanding of this solution, Figure 3As shown, this scheme is explained in a chronological logic. After acquiring the target object, the target object is first subjected to sample corrosion treatment, and then the metallographic microscopic image is observed and recorded using a metallographic microscope. After recording, plasma treatment, sample surface functionalization treatment and fluorescein labeling treatment are carried out in sequence. Subsequently, observation is performed using a fluorescence microscope to confirm whether the labeling is successful. If the labeling is unsuccessful, it is necessary to return to the sample corrosion step and start again; if the labeling is successful, the fluorescence microscopic image is recorded. Afterwards, the image is analyzed and different images are superimposed. Finally, the carbide grading model is applied to determine the carbide grading of the target object.

[0068] The above-mentioned metal carbide classification method first captures the fluorescence microscopic image and metallographic microscopic image of the target object. Subsequently, the two images are superimposed to generate a first image. Next, the first image is marked and input into the carbide classification model to finally obtain the carbide classification result of the target object. This scheme omits the processing step of the preliminary screening model, effectively avoids the image error introduced by the preliminary screening model processing, and can clearly and accurately display key information such as the morphology and distribution of carbides, thereby improving the accuracy of the final carbide classification result.

[0069] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0070] Corresponding to the metal carbide classification method described in the above embodiment, Figure 4 A structural block diagram of a metal carbide grading device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0071] See also Figure 4 , the metal carbide classification device in the embodiment of the present application may include: The data acquisition module 201 is used to acquire a fluorescence microscopic image and a metallographic microscopic image of a target object.

[0072] The image superposition module 202 is used to superimpose the fluorescence microscopic image and the metallographic microscopic image to obtain a first image.

[0073] The result output module 203 is used to mark the distribution and size of the carbides in the first image, and input the marked first image into the carbide classification model to obtain the classification result of the target object.

[0074] Exemplarily, the image overlay module 202 may be used to: The fluorescence microscopic image is registered according to the scale and size of the metallographic microscopic image to obtain a second image.

[0075] The second image and the metallographic microscope image are superimposed and fused to obtain the first image.

[0076] Exemplarily, the carbide classification model is a neural network model.

[0077] The neural network model uses a Canny edge detection algorithm or a Sobel operator detection algorithm to determine the distribution area and size of the carbides according to the marks in the first image.

[0078] Based on the distribution area and size of the carbides, the first image is classified to determine a classification result of the first image.

[0079] Exemplarily, determining the distribution area and size of carbides according to the marks in the first image includes: Based on the markings in the first image, noisy distribution areas of carbides are determined.

[0080] The noise points in the noisy distribution area are removed to obtain the distribution area of ​​carbides.

[0081] Based on the distribution area of ​​carbides, the size of the carbides is determined.

[0082] Exemplarily, the data acquisition module 201 may be used for: The target object is corroded by nitric acid alcohol corrosive solution.

[0083] The etched target object is sequentially subjected to ultrasonic cleaning, plasma treatment and immersion in a silane coupling agent.

[0084] Cures the silane coupling agent on the surface of the target object.

[0085] The target object that has completed the curing operation is fluorescently marked with a fluorescent agent, and the unreacted fluorescent agent is washed away after the marking is completed.

[0086] The fluorescently labeled target object is observed through a fluorescence microscope to obtain a fluorescence microscopic image.

[0087] Exemplarily, the silane coupling agent is a mixture of a phosphate buffer solution and an ethanol solution of 3-aminopropyltriethoxysilane.

[0088] The fluorescent agent is fluorescein isothiocyanate.

[0089] Exemplarily, the data acquisition module 201 may be used for: The target object after being corroded by nitric acid alcohol corrosive solution is observed through a metallographic microscope to obtain a metallographic microscopic image.

[0090] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0091] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0092] The present application also provides a terminal device, see Figure 5 The terminal device 300 may include: at least one processor 310 and a memory 320, wherein the memory 320 is used to store a computer program 321, and the processor 310 is used to call and run the computer program 321 stored in the memory 320 to implement the steps in any of the above-mentioned method embodiments, for example Figure 1 Steps 101 to 103 in the illustrated embodiment. Alternatively, when the processor 310 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 4 The functions of each module are shown.

[0093] Exemplarily, the computer program 321 may be divided into one or more modules / units, one or more modules / units are stored in the memory 320, and are executed by the processor 310 to complete the present application. The one or more modules / units may be a series of computer program segments that can complete specific functions, and the program segments are used to describe the execution process of the computer program in the terminal device 300.

[0094] Those skilled in the art will understand that Figure 5 It is only an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0095] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0096] The memory 320 may be an internal storage unit of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 320 is used to store the computer program and other programs and data required by the terminal device. The memory 320 may also be used to temporarily store data that has been output or is to be output.

[0097] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0098] The metal carbide grading method provided in the embodiment of the present application can be applied to terminal devices such as computers, wearable devices, vehicle-mounted devices, tablet computers, laptops, netbooks, etc. The embodiment of the present application does not impose any restrictions on the specific type of terminal devices.

[0099] The embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in each embodiment of the above-mentioned metal carbide grading method can be implemented.

[0100] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in each embodiment of the above-mentioned metal carbide classification method when executing the computer program product.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0102] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0104] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A metal carbide classification method, characterized in that: include: Acquire fluorescence microscopic images and metallographic microscopic images of target objects; Superimposing the fluorescence microscopic image and the metallographic microscopic image to obtain a first image; The distribution and size of the carbides in the first image are marked, and the marked first image is input into a carbide classification model to obtain a classification result of the target object.

2. The metal carbide classification method according to claim 1, characterized in that: The step of superimposing the fluorescence microscopic image and the metallographic microscopic image to obtain a first image comprises: Registering the fluorescence microscopic image according to the proportion and size of the metallographic microscopic image to obtain a second image; The second image and the metallographic microscopic image are superimposed and fused to obtain the first image.

3. The metal carbide classification method according to claim 1, characterized in that: The carbide classification model is a neural network model; The neural network model uses a Canny edge detection algorithm or a Sobel operator detection algorithm to determine the distribution area and size of carbides according to the marks in the first image; Based on the distribution area and size of the carbides, the first image is classified to determine a classification result of the first image.

4. The metal carbide classification method according to claim 3, characterized in that: Determining the distribution area and size of carbides according to the marks in the first image includes: Determining a noisy distribution area of ​​carbides based on the markers in the first image; removing noise points in the noisy distribution area to obtain the distribution area of ​​the carbide; Based on the distribution area of ​​the carbides, the size of the carbides is determined.

5. The metal carbide classification method according to claim 1, characterized in that: The step of obtaining a fluorescent microscopic image of a target object comprises: corroding the target object by using nitric acid alcohol corrosive solution; The target object after corrosion is sequentially subjected to ultrasonic cleaning, plasma treatment and immersion in a silane coupling agent; curing the silane coupling agent on the surface of the target object; Using a fluorescent agent to fluorescently mark the target object after the curing operation, and washing away the unreacted fluorescent agent after the marking is completed; The fluorescently labeled target object is observed through a fluorescent microscope to obtain the fluorescent microscopic image.

6. The metal carbide classification method according to claim 5, characterized in that: The silane coupling agent is a mixture of phosphate buffer and ethanol solution of 3-aminopropyltriethoxysilane; The fluorescent agent is fluorescein isothiocyanate.

7. The metal carbide classification method according to claim 5, characterized in that: The step of obtaining a metallographic microscopic image of a target object comprises: The metallographic microscopic image is obtained by observing the target object after being corroded by nitric acid alcohol corrosive solution through a metallographic microscope.

8. A metal carbide classification device, characterized in that: include: A data acquisition module, used to acquire a fluorescent microscopic image and a metallographic microscopic image of a target object; An image superposition module, used for superimposing the fluorescence microscopic image and the metallographic microscopic image to obtain a first image; The result output module is used to mark the distribution and size of the carbides in the first image, and input the marked first image into the carbide classification model to obtain the classification result of the target object.

9. A terminal device, comprising: A processor and a memory, wherein the memory stores a computer program that can be run on the processor, wherein the processor implements the metal carbide grading method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the metal carbide classification method according to any one of claims 1 to 7 is implemented.

Images