CN108509958A - Defect type detection method, device, electronic equipment and medium - Google Patents

Defect type detection method, device, electronic equipment and medium Download PDF

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Publication number
CN108509958A
CN108509958A CN201810276600.9A CN201810276600A CN108509958A CN 108509958 A CN108509958 A CN 108509958A CN 201810276600 A CN201810276600 A CN 201810276600A CN 108509958 A CN108509958 A CN 108509958A
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China
Prior art keywords
detected
target
defect type
color image
sample
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CN201810276600.9A
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Chinese (zh)
Inventor
黄献德
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Beijing Kingsoft Internet Security Software Co Ltd
Kingsoft Corp Ltd
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Kingsoft Corp Ltd
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Priority to CN201810276600.9A priority Critical patent/CN108509958A/en
Publication of CN108509958A publication Critical patent/CN108509958A/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • G06K9/4652Extraction of features or characteristics of the image related to colour
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0454Architectures, e.g. interconnection topology using a combination of multiple neural nets
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0004Industrial image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/90Determination of colour characteristics

Abstract

An embodiment of the present invention provides a kind of defect type detection method, device, electronic equipment and medium, this method to include:Obtain the bytecode of target to be detected, wherein above-mentioned target to be detected is:The program write based on default programming language;Based on the bytecode of the target to be detected obtained, the corresponding color image to be detected of target to be detected is obtained;Based on the color image to be detected and preset defect type detection model obtained, determine that target to be detected whether there is defect, and when determining existing defects, determine the corresponding defect type of target to be detected, wherein preset defect type detection model:Include the correspondence of each defect type and characteristics of image.To realize that the detection that whether there is defect to program to be detected is further realized when program existing defects to be detected, the detection to defect type.

Description

Defect type detection method, device, electronic equipment and medium
Technical field
The present invention relates to field of computer technology, more particularly to a kind of defect type detection method, device, electronic equipment And medium.
Background technology
In some situations, some are write completion program be deployed to corresponding system after, when the above-mentioned journey for writing completion When sequence existing defects, the system based on the above-mentioned program for writing completion of the deployment carries out having security risk when corresponding operating. For example, the above-mentioned program for writing completion is the intelligent contract on block chain, when the intelligent contract there will be defect is deployed in block After chain, trading activity caused by the block chain based on the deployment intelligence contract is likely to occur the case where can not being repaired, such as: After block chain initiation moneytary operations of the initiator person that merchandises based on the above-mentioned deployment intelligence contract, even if the friendship of the moneytary operations Easy recipient is wrong, which can not be withdrawn.The above situation so that it is hidden that the transaction in block chain has safety Suffer from.As it can be seen that before disposing some and writing the program of completion, it is most important to the analysis of the correctness of above procedure, in a side In face, the detection to the defect of above procedure, and type to existing defect detection, to the correct of intelligence above procedure It is most important in the analytic process of property.
So, how before disposing above procedure, determine program whether there is defect, and then determine defect type at For urgent problem to be solved.
Invention content
The embodiment of the present invention is designed to provide a kind of defect type detection method, device, electronic equipment and medium, with The problem of solving how before disposing above procedure, determining that program whether there is defect, and then determine the type of defect.Specifically Technical solution is as follows:
In a first aspect, an embodiment of the present invention provides a kind of defect type detection method, the method includes:
Obtain the bytecode of target to be detected, wherein the target to be detected is:It is write based on default programming language Program;
Based on the bytecode of the target to be detected obtained, the corresponding chromaticity diagram to be detected of the target to be detected is obtained Picture;
Based on the color image to be detected and preset defect type detection model obtained, the mesh to be detected is determined Mark whether there is defect, and when determining existing defects, determine the corresponding defect type of the target to be detected, wherein described Preset defect type detection model:Include the correspondence of each defect type and characteristics of image.
Optionally, the default programming language includes solidity language, and the target to be detected includes intelligent contract.
Optionally, the bytecode of the target to be detected is a string of character strings;
The bytecode based on the target to be detected obtained obtains the corresponding color to be detected of the target to be detected Image, including:
The bytecode of the target to be detected obtained is translated into RGB color generation based on the preset RGB color table of comparisons Code, wherein the preset RGB color table of comparisons includes:The correspondence of each RGB color code and character substring;
By the RGB color code, unloading is the corresponding color image to be detected of the target to be detected.
Optionally, the preset defect type detection model is preset convolutional neural networks model;
Described based on the color image to be detected obtained and preset defect type detection model, determine described in wait for It detects target and whether there is defect, and when determining existing defects, before determining the corresponding defect type of the target to be detected, The method further includes:
Establish the process of the preset convolutional neural networks model, wherein the process includes:
Obtain initial convolutional neural networks model;
Obtain the bytecode of multiple sample objects, wherein the sample object is:It is compiled based on the default programming language The program write;
Based on the bytecode of each sample object, the corresponding sample color image of each sample object is obtained;
Obtain the corresponding calibration information of each sample color image, wherein the calibration information includes:Corresponding sample color Coloured picture is as corresponding defect type, as prediction defect type;
Based on being wrapped in each sample color image and the corresponding calibration information of each sample color image obtained The prediction defect type included, the training initial convolutional neural networks model, obtains preset convolutional neural networks model.
Optionally, the initial convolutional neural networks model includes feature extraction layer and tagsort layer;
It is described based in each sample color image obtained and the corresponding calibration information of each sample color image Included prediction defect type, the training initial convolutional neural networks model, obtains preset convolutional neural networks mould Type, including:
The each sample color image obtained is inputted into the feature extraction layer, extracts the sample of the sample color image Characteristics of image;
The sample image feature extracted is inputted into the tagsort layer, it is corresponding current to obtain the sample color image Defect type;
The current defect type obtained is matched with corresponding prediction defect type;
When successful match, the preset convolutional Neural net for including the feature extraction layer and the tagsort layer is obtained Network model;
When it fails to match, the parameter of the feature extraction layer and the tagsort layer is adjusted, return execution is described will The each sample color image obtained inputs the feature extraction layer, extracts the sample image feature of the sample color image;
Until when successful match, the preset convolutional Neural for including the feature extraction layer and the tagsort layer is obtained Network model.
Optionally, described based on each sample color image obtained and the corresponding mark of each sample color image Determine prediction defect type included in information, the training initial convolutional neural networks model obtains preset convolution god After network model, the method further includes:
Based on default testing process, the accuracy of the definitive result of the preset convolutional neural networks model is detected, is obtained Obtain testing result;
Obtain the adjust instruction for the testing result, wherein the adjust instruction carries required adjustment result;
Based on the adjustment obtained as a result, being adjusted to the preset convolutional neural networks model.
Optionally, described based on the color image to be detected obtained and preset defect type detection model, it determines The target to be detected whether there is defect, and when determining existing defects, determine the corresponding defect class of the target to be detected Type, including:
The color image to be detected is inputted into the preset convolutional neural networks model, the preset convolutional Neural Network model extracts image to be detected feature of the color image to be detected;And it is special based on the described image to be detected extracted Sign determines that the target to be detected whether there is defect, and when determining the target existing defects to be detected, is waited for described in determination Detect the corresponding defect type of target.
Optionally, the bytecode for obtaining target to be detected, including:
It obtains and the detection that the target to be detected is detected is instructed, wherein the detection instruction includes described to be checked Survey the bytecode of target;
Based on the detection instruction obtained, the bytecode of the target to be detected is obtained.
Second aspect, an embodiment of the present invention provides a kind of defect type detection device, described device includes:
First obtains module, the bytecode for obtaining target to be detected, wherein the target to be detected is:Based on pre- If the program that programming language is write;
Second obtains module, for the bytecode based on the target to be detected obtained, obtains the target pair to be detected The color image to be detected answered;
Determining module, for based on the color image to be detected and preset defect type detection model obtained, really The fixed target to be detected whether there is defect, and when determining existing defects, determine the corresponding defect of the target to be detected Type, wherein the preset defect type detection model:Include the correspondence of each defect type and characteristics of image.
Optionally, the default programming language includes solidity language, and the target to be detected includes intelligent contract.
Optionally, the bytecode of the target to be detected is a string of character strings;
Described second obtains module, is specifically used for
The bytecode of the target to be detected obtained is translated into RGB color generation based on the preset RGB color table of comparisons Code, wherein the preset RGB color table of comparisons includes:The correspondence of each RGB color code and character substring;
By the RGB color code, unloading is the corresponding color image to be detected of the target to be detected.
Optionally, the preset defect type detection model is preset convolutional neural networks model;
Described device further includes:
Model building module, for being examined based on the color image to be detected obtained and preset defect type described Model is surveyed, determines that the target to be detected whether there is defect, and when determining existing defects, determine the target pair to be detected Before the defect type answered, the preset convolutional neural networks model is established, wherein the model building module includes:
Second obtaining unit, for obtaining initial convolutional neural networks model;
Third obtaining unit, the bytecode for obtaining multiple sample objects, wherein the sample object is:Based on institute State the program that default programming language is write;
4th obtaining unit is used for the bytecode based on each sample object, obtains the corresponding sample of each sample object Color image;
5th obtaining unit, for obtaining the corresponding calibration information of each sample color image, wherein the calibration information Including:The corresponding defect type of corresponding sample color image, as prediction defect type;
Training obtains unit, for based on each sample color image obtained and each sample color image correspondence Calibration information in included prediction defect type, the training initial convolutional neural networks model obtains preset volume Product neural network model.
Optionally, the initial convolutional neural networks model includes feature extraction layer and tagsort layer;
The training obtains unit, is specifically used for
The each sample color image obtained is inputted into the feature extraction layer, extracts the sample of the sample color image Characteristics of image;
The sample image feature extracted is inputted into the tagsort layer, it is corresponding current to obtain the sample color image Defect type;
The current defect type obtained is matched with corresponding prediction defect type;
When successful match, the preset convolutional Neural net for including the feature extraction layer and the tagsort layer is obtained Network model;
When it fails to match, the parameter of the feature extraction layer and the tagsort layer is adjusted, return execution is described will The each sample color image obtained inputs the feature extraction layer, extracts the sample image feature of the sample color image;
Until when successful match, the preset convolutional Neural for including the feature extraction layer and the tagsort layer is obtained Network model.
Optionally, the model building module further includes:
Detection unit, for described based on each sample color image obtained and each sample color image pair Included prediction defect type in the calibration information answered, the training initial convolutional neural networks model, obtains preset After convolutional neural networks model, based on default testing process, that detects the preset convolutional neural networks model determines knot The accuracy of fruit obtains testing result;
6th obtaining unit, for obtaining the adjust instruction for the testing result, wherein the adjust instruction carries Required adjustment result;
Adjustment unit, for based on the adjustment obtained as a result, being adjusted to the preset convolutional neural networks model It is whole.
Optionally, the determining module, is specifically used for
The color image to be detected is inputted into the preset convolutional neural networks model, the preset convolutional Neural Network model extracts image to be detected feature of the color image to be detected;And it is special based on the described image to be detected extracted Sign determines that the target to be detected whether there is defect, and when determining the target existing defects to be detected, is waited for described in determination Detect the corresponding defect type of target.
Optionally, described first module is obtained, be specifically used for
It obtains and the detection that the target to be detected is detected is instructed, wherein the detection instruction includes described to be checked Survey the bytecode of target;
Based on the detection instruction obtained, the bytecode of the target to be detected is obtained.
The third aspect, an embodiment of the present invention provides a kind of electronic equipment, including processor, communication interface, memory and Communication bus, wherein processor, communication interface, memory complete mutual communication by communication bus;
Memory, for storing computer program;
Processor when for executing the computer program stored on memory, realizes what the embodiment of the present invention was provided Any of the above-described defect type detection method step.
Fourth aspect, an embodiment of the present invention provides a kind of computer readable storage medium, the computer-readable storage Dielectric memory contains computer program, and it is upper to realize that the embodiment of the present invention is provided when the computer program is executed by processor State any defect type detection method step.
5th aspect, an embodiment of the present invention provides a kind of computer programs, wherein the computer program is by processor Any of the above-described defect type detection method step that the embodiment of the present invention is provided is realized when execution.
In the embodiment of the present invention, the bytecode of target to be detected is obtained, wherein above-mentioned target to be detected is:Based on default The program that programming language is write;Based on the bytecode of the target to be detected obtained, it is corresponding to be checked to obtain target to be detected Survey color image;Based on the color image to be detected and preset defect type detection model obtained, mesh to be detected is determined Mark whether there is defect, and when determining existing defects, determine the corresponding defect type of target to be detected, wherein preset to lack Fall into type detection model:Include the correspondence of each defect type and characteristics of image.
In the embodiment of the present invention, based on the bytecode of obtained target to be detected, the target to be detected of acquisition is corresponding to be waited for Detect color image, in turn, based on color image to be detected and preset defect type detection model included it is each lack Fall into the correspondence of type and characteristics of image, it may be determined that go out target to be detected and whether there is defect, and is to be detected determining When target existing defects, the corresponding defect type of target to be detected is determined, realize to target to be detected with the presence or absence of defect Detection is further realized when target existing defects to be detected, the detection to defect type, to solve how in deployment Before stating program, the problem of determining that program whether there is defect, and then determine the type of defect.Certainly, implement appointing for the present invention One product or method must be not necessarily required to reach all the above advantage simultaneously.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with Obtain other attached drawings according to these attached drawings.
A kind of flow diagram for defect type detection method that Fig. 1 is provided by the embodiment of the present invention;
A kind of flow diagram for establishing preset convolutional neural networks model that Fig. 2 is provided by the embodiment of the present invention;
A kind of structural schematic diagram for defect type detection device that Fig. 3 is provided by the embodiment of the present invention;
A kind of structural schematic diagram for the model building module that Fig. 4 is provided by the embodiment of the present invention;
The structural schematic diagram for a kind of electronic equipment that Fig. 5 is provided by the embodiment of the present invention.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other Embodiment shall fall within the protection scope of the present invention.
An embodiment of the present invention provides a kind of defect type detection method, device, electronic equipment and medium, with solve how Before disposing above procedure, the problem of determining that program whether there is defect, and then determine the type of defect.
As shown in Figure 1, an embodiment of the present invention provides a kind of defect type detection method, this method includes:
S101:Obtain the bytecode of target to be detected;
Wherein, above-mentioned target to be detected is:The program write based on default programming language;Wherein, above-mentioned default programming Language can be solidity language and Javascript language etc..
It is understood that the defect type detection method that the embodiment of the present invention is provided, can be applied to any electronics In equipment, above-mentioned electronic equipment can be computer and smart mobile phone etc..Realize the defect type that the embodiment of the present invention is provided The functional software of detection method can exist in the form of special client software, can also be with the plug-in unit of client software Form exists.
In the embodiment of the present invention, above-mentioned electronic equipment can directly obtain the bytecode of above-mentioned target to be detected (Bytecode), for example, after receiving specific instruction, that is, the bytecode of target to be detected is obtained.In oneainstance, above-mentioned Electronic equipment can be server, and above-mentioned server can receive the inspection about target to be detected that user is sent by client Instruction is surveyed, above-mentioned detection instruction can carry the bytecode of above-mentioned target to be detected;Alternatively, above-mentioned detection instruction can carry The mark of target to be detected is stated, above-mentioned server obtains the mark of above-mentioned target to be detected, the mark based on above-mentioned target to be detected Know, obtains the bytecode of target to be detected.
S102:Based on the bytecode of the target to be detected obtained, the corresponding chromaticity diagram to be detected of target to be detected is obtained Picture;
It in one implementation, can be with above-mentioned to be checked after electronic equipment obtains the bytecode of above-mentioned target to be detected The bytecode for surveying target, obtains the corresponding color image to be detected of target to be detected.In oneainstance, above-mentioned electronic equipment sheet In ground or the storage device communicated to connect with electronic equipment, it is stored with the bytecode of each target and the correspondence of color image, Electronic equipment can be based on above-mentioned correspondence and above-mentioned target to be detected bytecode, obtain that target to be detected is corresponding to be waited for Detect color image.Wherein, above-mentioned target is:The program write based on default programming language.
S103:Based on the color image to be detected and preset defect type detection model obtained, determine to be detected Target whether there is defect, and when determining existing defects, determine the corresponding defect type of target to be detected.
Wherein, preset defect type detection model:Include the correspondence of each defect type and characteristics of image.
In the embodiment of the present invention, above-mentioned preset defect type detection model can be:Based on sample color image and The model of machine learning algorithm training gained, wherein above-mentioned sample color image is:Bytecode based on sample object is obtained Image, above-mentioned sample object is:The program write based on default programming language.Wherein, above-mentioned machine learning algorithm can be with For convolutional neural networks algorithm, deep learning algorithm and algorithm of support vector machine etc..
In one implementation, training obtains above-mentioned preset defect type detection model, needs to obtain first multiple Sample data, such as sample color image, wherein each sample color image corresponds to the bytecode of a sample object;In turn, it obtains The corresponding calibration information of each sample color image, wherein above-mentioned calibration information includes corresponding sample color image pair The prediction defect type answered, drawbacks described above type are:The type for the defect that the corresponding sample object of sample color image is included. Above-mentioned calibration information can be what model foundation personnel demarcated manually.
It, can be with base after obtaining above-mentioned sample color image and the corresponding prediction defect type of each sample color image Sample color image and the corresponding prediction defect type of each sample color image are stated in above-mentioned, initial defect type is examined Model is surveyed to be trained.In training process, the parameter in above-mentioned initial defect type detection model, Zhi Daochu can be constantly adjusted After the defect type detection model convergence of beginning, the preset defect type detection model of training gained is obtained, wherein this is preset Defect type detection model includes the correspondence of each defect type and characteristics of image.
In one case, the initial convergent mark of defect type detection model, Ke Yishi:Initial defect type inspection The current detection obtained for each present color image that model convergence is exported is surveyed as a result, i.e. current defect type, with pair The identical probability of prediction defect type for including in calibration information is answered, the first predetermined threshold is more than;In another case, initially The convergent mark of defect type detection model, Ke Yishi:Using preset loss function, initial defect type detection is calculated The current defect type of model output obtained for each present color image, with the prediction for including in corresponding calibration information Difference between defect type, the difference calculated are less than the second predetermined threshold.
It is subsequent, electronic equipment be based on above-mentioned preset defect type detection model included in each defect type with The correspondence of characteristics of image, and the color image to be detected that is obtained, obtain testing result, i.e. determination obtains mesh to be detected Mark whether there is defect, and when determining existing defects, and determination obtains the corresponding defect type of target to be detected.On specifically, Preset defect type detection model can be based on by stating electronic equipment, extract the characteristics of image of color image to be detected, Jin Erji It is and each included in above-mentioned preset defect type detection model in the characteristics of image of the color image to be detected extracted The correspondence of defect type and characteristics of image, obtains testing result.
In one implementation, drawbacks described above can characterize the loophole present in target to be detected, drawbacks described above type For the type of the loophole present in target to be detected, in oneainstance, above-mentioned target to be detected can be intelligent contract.
Drawbacks described above type can be varied, including but not limited to:can be killed by arbitrary Addresses (indefinitely locking fund), have no way to release Ether after a certain Execution state (intelligent contract is accidentally leaked into arbitrary user), release Ether to arbitrary (transaction is suitable by addresses carelessly (can be by any address termination) Transaction-Ordering Dependence Sequence rely on), Timestamp Dependence (timestamp dependence), Mishandled Exceptions (improper exception treatment) And TheDao hack (theft of decentralization tissue ether coin) etc..
In the embodiment of the present invention, based on the bytecode of obtained target to be detected, the target to be detected of acquisition is corresponding to be waited for Detect color image, in turn, based on color image to be detected and preset defect type detection model included it is each lack Fall into the correspondence of type and characteristics of image, it may be determined that go out target to be detected and whether there is defect, and is to be detected determining When target existing defects, the corresponding defect type of target to be detected is determined, realize to target to be detected with the presence or absence of defect Detection is further realized when target existing defects to be detected, the detection to defect type.
In one implementation, default programming language may include solidity language, and target to be detected may include Intelligent contract.Wherein, above-mentioned intelligent contract is the program write based on above-mentioned solidity language.
In one implementation, the bytecode for obtaining target to be detected may include:
It obtains and the detection that target to be detected is detected is instructed, wherein detection instruction includes the byte of target to be detected Code;
Based on the detection instruction obtained, the bytecode of target to be detected is obtained.
In oneainstance, electronic equipment can be server, when server obtains pair that user is sent by client After the detection instruction that target to be detected is detected, extracted from above-mentioned detection instruction to be detected entrained by detection instruction The bytecode of target, and then for the bytecode of the target to be detected, execute subsequent defect type testing process.
It is subsequent, when server passes through the bytecode of preset defect type detection model and the target to be detected, determination After going out the testing result of target to be detected, the testing result for the target to be detected determined can be sent to above-mentioned client End.After above-mentioned client receives above-mentioned testing result, it can continue to show above-mentioned testing result, so that user checks, user It can be based on above-mentioned testing result and carry out subsequent operation, for example, it is determined whether disposing above-mentioned wait on the block chain in ether mill Detect target.Wherein, above-mentioned testing result includes:Detected above-mentioned target to be detected with the presence or absence of defect as a result, with And when determining above-mentioned result existing defects to be detected, identified defect type.
The embodiment of the present invention detects the target to be detected with the presence or absence of scarce before user disposes above-mentioned target to be detected It falls into, and when determining the target existing defects to be detected, determines the type of the defect present in target to be detected.It can be to avoid There will be the target deployments to be detected of defect for appearance on the block chain in ether mill, to a certain extent, avoids the area in ether mill There are security risks for block chain.Also, the embodiment of the present invention determines the type of the defect present in target to be detected, is more convenient for out Hair personnel modify to target to be detected perfect.
In one implementation, the bytecode of the target to be detected is a string of character strings;
Based on the bytecode of the target to be detected obtained, the step of the corresponding color image to be detected of target to be detected is obtained Suddenly, may include:
Based on preset RGB (RedGreenBlue, RGB) color chart, by the word of the target to be detected obtained Code is saved, RGB color code is translated to;
Wherein, the preset RGB color table of comparisons includes:The correspondence of each RGB color code and character substring;
By RGB color code, unloading is the corresponding color image to be detected of target to be detected.
It is understood that the bytecode of above-mentioned target to be detected is hexadecimal, and above-mentioned preset RGB color pair Also it is hexadecimal according to the character substring in table, is based on the above-mentioned preset RGB color table of comparisons, it can be directly by mesh to be detected Target bytecode translates to RGB color code.
Specifically, can start from character string, i.e., the initial character of the bytecode of above-mentioned target to be detected, every six characters are made It for one group of character substring, is matched with the preset RGB color table of comparisons, by the corresponding RGB face of the character substring of successful match Color code.Such as:" 606060 " corresponding RGB color code in the bytecode of above-mentioned target to be detected be RGB (96,96, 96), " 405260 " corresponding RGB color code in the bytecode of above-mentioned target to be detected is RGB (64,82,96) etc..
Subsequent, electronic equipment can determine the corresponding RGB color of RGB color code, and then based on determined by The corresponding RGB color of RGB color code obtains the corresponding color image to be detected of target to be detected.Wherein, in order to improve The size of detection efficiency, above-mentioned color image to be detected can be pre-set dimension, which can be with sample color image Size it is identical, above-mentioned sample color image is:Training obtains the image that above-mentioned preset defect type detection model is utilized.
In one implementation, above-mentioned preset defect type detection model can be preset convolutional neural networks mould Type;
Based on the color image to be detected and preset defect type detection model obtained, target to be detected is determined With the presence or absence of defect, and when determining existing defects, before determining the corresponding defect type of target to be detected, the method may be used also To include:
Establish the process of preset convolutional neural networks model, wherein as shown in Fig. 2, the process may include:
S201:Obtain initial convolutional neural networks model;
S202:Obtain the bytecode of multiple sample objects;
Wherein, sample object is:The program write based on default programming language;
S203:Based on the bytecode of each sample object, the corresponding sample color image of each sample object is obtained;
S204:Obtain the corresponding calibration information of each sample color image;
Wherein, calibration information includes:The corresponding defect type of corresponding sample color image, as prediction defect type; The defect type is:The type for the defect that the corresponding sample object of sample color image is included.
S205:Based in each sample color image and the corresponding calibration information of each sample color image obtained Included prediction defect type, the training initial convolutional neural networks model, obtains preset convolutional neural networks mould Type.
It is understood that the quantity of the sample color image of the above-mentioned initial convolutional neural networks model of training is more, The preset convolutional neural networks model of training gained is more stable, further, utilizes above-mentioned preset convolutional neural networks mould Type, the target to be detected determined with the presence or absence of defect as a result, and when determining existing defects, identified defect class Type is more accurate.
In oneainstance, in the above-mentioned initial convolutional neural networks model of training, the sample color image that is obtained Quantity may be less, such as less than certain predetermined threshold value, at this point it is possible to using K-fold modes.Utilize obtained sample Color image and the corresponding calibration information of each sample color image, the above-mentioned initial convolutional neural networks model of training, with Obtain initial convolutional neural networks model.
In addition, being based on preset convolutional neural networks model, determine that target to be detected whether there is defect, and in determination When existing defects, its defect type is determined, can ensure the accuracy of determined the above results to a certain extent, this is because Training obtains the sample color image of preset convolutional neural networks model, is the corresponding image of true sample object, can To characterize the various information of sample object well.
In embodiments of the present invention, above-mentioned default programming language can be solidity language and Javascript language Deng.Above-mentioned sample object can be intelligent contract, at this point, the above-mentioned intelligent contract as sample object can be deployed in Intelligent contract too on the block chain in mill.At this point, electronic equipment can obtain the address of above-mentioned intelligent contract by reptile software, And then based on the address obtained, by ether mill API, (Application Program Interface, application program connect Mouthful) get the corresponding bytecode of above-mentioned intelligent contract.
Above-mentioned convolutional neural networks model can be:VGG16/19 network models, Inception v3 network models and Resnet network models etc..
In one implementation, initial convolutional neural networks model may include feature extraction layer and tagsort Layer;
It is described based in each sample color image obtained and the corresponding calibration information of each sample color image Included prediction defect type, the initial convolutional neural networks model of training, obtains preset convolutional neural networks model, can To include:
The each sample color image input feature vector extract layer that will be obtained, extracts the sample image of the sample color image Feature;
By the sample image feature input feature vector extracted classification layer, the corresponding current defect of sample color image is obtained Type;
The current defect type obtained is matched with corresponding prediction defect type;
When successful match, the preset convolutional neural networks model for including feature extraction layer and tagsort layer is obtained;
When it fails to match, the parameter of feature extraction layer and tagsort layer is adjusted, returning to execute above-mentioned will be obtained Each sample color image input feature vector extract layer, the step of extracting the sample image feature of the sample color image;
Until when successful match, the preset convolutional neural networks mould for including feature extraction layer and tagsort layer is obtained Type.
It is above-mentioned to be by the current defect type obtained and the corresponding matched process of prediction defect type progress, The difference of the current defect type and corresponding prediction defect type that obtain is calculated using preset loss function, it is poor when calculating Value allows default in loss range, it is determined that successful match, when institute's calculating difference does not allow default in loss range, then really Surely match it is unsuccessful, at this point it is possible to based on by the current defect type of acquisition and it is corresponding prediction defect type difference become smaller Principle, adjust features described above extract layer and tagsort layer parameter;It is above-mentioned each by what is obtained that execution is returned to again Sample color image inputs the feature extraction layer, the step of extracting the sample image feature of the sample color image.In one kind In realization method, the parameter of gradient descent method adjustment features described above extract layer and tagsort layer can be utilized.
It in one implementation, can will be each during training above-mentioned initial convolutional neural networks model Sample image frame inputs above-mentioned initial convolutional neural networks model, to be carried out to above-mentioned initial convolutional neural networks model Training;Can also be first from the bytecode of above-mentioned sample object, random or sequence chooses the sample object of predetermined quantity The bytecode of the sample object of selected predetermined quantity is inputted above-mentioned initial convolutional neural networks model by bytecode, with Above-mentioned initial convolutional neural networks model is trained, preset convolutional neural networks model is obtained.
In one implementation, it in order to obtain better preset convolutional neural networks model, is preset in training Convolutional neural networks model, which can be optimized.Specifically, being based on institute described Included prediction defect class in each sample color image and the corresponding calibration information of each sample color image that obtain Type, the training initial convolutional neural networks model, after obtaining preset convolutional neural networks model, the method may be used also To include:
Based on default testing process, the accuracy of preset convolutional neural networks model definitive result is detected, is detected As a result;
Obtain the adjust instruction for testing result, wherein adjust instruction carries required adjustment result;
Based on the adjustment obtained as a result, being adjusted to preset convolutional neural networks model.
Wherein, after obtaining preset convolutional neural networks model, electronic equipment can continue known defect type The corresponding color image of target inputs above-mentioned preset convolutional neural networks model, is exported as a result, by above-mentioned output result It is matched with the known defect type of the color image, preset convolutional neural networks model is determined really based on matching result The accuracy for determining result, obtains testing result.When matching rate is less than preset matching threshold value, model foundation personnel tie according to detection Fruit and experience determine corresponding Adjusted Option, and determine adjustment as a result, and trigger carry adjustment result adjust instruction, Electronic equipment obtains the adjust instruction, wherein adjust instruction carries the above-mentioned preset convolutional neural networks model adjusted The adjustment of parameter and each parameter of required adjustment is as a result, for example:Parameter may include learning rate (learning rate), criticize The adjustment result of size (batch size), the number of plies of convolutional layer and majorized function etc., each parameter may include that adjustment is learned Habit rate is to xx values, and big as low as yy values, etc. are criticized in adjustment, wherein above-mentioned majorized function may include Relu (Rectified Linear Units) activation primitive.In turn, electronic equipment can continue the preset convolutional neural networks mould after training adjustment Type.
In one implementation, described to be detected based on the color image to be detected obtained and preset defect type Model determines that target to be detected whether there is defect, and when determining existing defects, determines the corresponding defect class of target to be detected The step of type may include:
Color image to be detected is inputted into preset convolutional neural networks model, preset convolutional neural networks model extraction Image to be detected feature of color image to be detected;And based on image to be detected feature extracted, determine that target to be detected is No existing defects, and when determining target existing defects to be detected, determine the corresponding defect type of target to be detected.
In oneainstance, color image to be detected can be inputted preset convolutional neural networks model by electronic equipment Then feature extraction layer, feature extraction layer can will be extracted based on image to be detected feature for extracting color image to be detected Image to be detected feature inputs the tagsort layer of preset convolutional neural networks model, and tagsort layer is based on above-mentioned extracted Image to be detected feature determines that the target to be detected is determining above-mentioned target existing defects to be detected in turn with the presence or absence of defect When, determine the defect type of defect present in target to be detected, and export.
Corresponding to above method embodiment, an embodiment of the present invention provides a kind of defect type detection devices, such as Fig. 3 institutes Show, described device includes:
First obtains module 310, the bytecode for obtaining target to be detected, wherein the target to be detected is:It is based on The program that default programming language is write;
Second obtains module 320, for the bytecode based on the target to be detected obtained, obtains the target to be detected Corresponding color image to be detected;
Determining module 330, for based on the color image to be detected and preset defect type detection model obtained, It determines that the target to be detected whether there is defect, and when determining existing defects, determines that the target to be detected is corresponding and lack Fall into type, wherein the preset defect type detection model:Include the correspondence of each defect type and characteristics of image.
In the embodiment of the present invention, based on the bytecode of obtained target to be detected, the target to be detected of acquisition is corresponding to be waited for Detect color image, in turn, based on color image to be detected and preset defect type detection model included it is each lack Fall into the correspondence of type and characteristics of image, it may be determined that go out target to be detected and whether there is defect, and is to be detected determining When target existing defects, the corresponding defect type of target to be detected is determined, realize to target to be detected with the presence or absence of defect Detection is further realized when target existing defects to be detected, the detection to defect type.
In one implementation, the default programming language includes solidity language, and the target to be detected includes Intelligent contract.
In one implementation, the bytecode of the target to be detected is a string of character strings;
Described second obtains module 320, is specifically used for
The bytecode of the target to be detected obtained is translated into RGB color generation based on the preset RGB color table of comparisons Code, wherein the preset RGB color table of comparisons includes:The correspondence of each RGB color code and character substring;
By the RGB color code, unloading is the corresponding color image to be detected of the target to be detected.
In one implementation, the preset defect type detection model is preset convolutional neural networks model;
As shown in figure 4, described device can also include:
Model building module 410, for described based on the color image to be detected obtained and preset defect class Type detection model determines that the target to be detected whether there is defect, and when determining existing defects, determines the mesh to be detected Before marking corresponding defect type, the process of the preset convolutional neural networks model is established, wherein the model foundation mould Block 410 includes:
Second obtaining unit 411, for obtaining initial convolutional neural networks model;
Third obtaining unit 412, the bytecode for obtaining multiple sample objects, wherein the sample object is:It is based on The program that the default programming language is write;
4th obtaining unit 413 is used for the bytecode based on each sample object, obtains the corresponding sample of each sample object Present color image;
5th obtaining unit 414, for obtaining the corresponding calibration information of each sample color image, wherein the calibration Information includes:The corresponding defect type of corresponding sample color image, as prediction defect type;
Training obtains unit 415, for based on each sample color image and each sample color image obtained Included prediction defect type in corresponding calibration information, the training initial convolutional neural networks model, is preset Convolutional neural networks model.
In one implementation, the initial convolutional neural networks model includes feature extraction layer and tagsort Layer;
The training obtains unit, is specifically used for
The each sample color image obtained is inputted into the feature extraction layer, extracts the sample of the sample color image Characteristics of image;
The sample image feature extracted is inputted into the tagsort layer, it is corresponding current to obtain the sample color image Defect type;
The current defect type obtained is matched with corresponding prediction defect type;
When successful match, the preset convolutional Neural net for including the feature extraction layer and the tagsort layer is obtained Network model;
When it fails to match, the parameter of the feature extraction layer and the tagsort layer is adjusted, return execution is described will The each sample color image obtained inputs the feature extraction layer, extracts the sample image feature of the sample color image;
Until when successful match, the preset convolutional Neural for including the feature extraction layer and the tagsort layer is obtained Network model.
In one implementation, the model building module further includes:
Detection unit, for described based on each sample color image obtained and each sample color image pair Included prediction defect type in the calibration information answered, the training initial convolutional neural networks model, obtains preset After convolutional neural networks model, based on default testing process, that detects the preset convolutional neural networks model determines knot The accuracy of fruit obtains testing result;
6th obtaining unit, for obtaining the adjust instruction for the testing result, wherein the adjust instruction carries Required adjustment result;
Adjustment unit, for based on the adjustment obtained as a result, being adjusted to the preset convolutional neural networks model It is whole.
In one implementation, the determining module 430, is specifically used for
The color image to be detected is inputted into the preset convolutional neural networks model, the preset convolutional Neural Network model extracts image to be detected feature of the color image to be detected;And it is special based on the described image to be detected extracted Sign determines that the target to be detected whether there is defect, and when determining the target existing defects to be detected, is waited for described in determination Detect the corresponding defect type of target.
In one implementation, described first module is obtained, be specifically used for
It obtains and the detection that the target to be detected is detected is instructed, wherein the detection instruction includes described to be checked Survey the bytecode of target;
Based on the detection instruction obtained, the bytecode of the target to be detected is obtained.
Corresponding to above method embodiment, the embodiment of the present invention additionally provides a kind of electronic equipment, as shown in figure 5, including Processor 510, communication interface 520, memory 530 and communication bus 540, wherein processor 510, communication interface 520, storage Device 530 completes mutual communication by communication bus 540,
Memory 530, for storing computer program;
Processor 510 when for executing the computer program stored on memory 530, realizes institute of the embodiment of the present invention Any of the above-described defect type detection method provided, the defect type detection method include:
Obtain the bytecode of target to be detected, wherein the target to be detected is:It is write based on default programming language Program;
Based on the bytecode of the target to be detected obtained, the corresponding chromaticity diagram to be detected of the target to be detected is obtained Picture;
Based on the color image to be detected and preset defect type detection model obtained, the mesh to be detected is determined Mark whether there is defect, and when determining existing defects, determine the corresponding defect type of the target to be detected, wherein described Preset defect type detection model:Include the correspondence of each defect type and characteristics of image.
In the embodiment of the present invention, based on the bytecode of obtained target to be detected, the target to be detected of acquisition is corresponding to be waited for Detect color image, in turn, based on color image to be detected and preset defect type detection model included it is each lack Fall into the correspondence of type and characteristics of image, it may be determined that go out target to be detected and whether there is defect, and is to be detected determining When target existing defects, the corresponding defect type of target to be detected is determined, realize to target to be detected with the presence or absence of defect Detection is further realized when target existing defects to be detected, the detection to defect type.
In one implementation, the default programming language includes solidity language, and the target to be detected includes Intelligent contract.
In one implementation, the bytecode of the target to be detected is a string of character strings;
The bytecode based on the target to be detected obtained obtains the corresponding color to be detected of the target to be detected Image, including:
The bytecode of the target to be detected obtained is translated into RGB color generation based on the preset RGB color table of comparisons Code, wherein the preset RGB color table of comparisons includes:The correspondence of each RGB color code and character substring;
By the RGB color code, unloading is the corresponding color image to be detected of target to be detected.
In one implementation, the preset defect type detection model is preset convolutional neural networks model;
Described based on the color image to be detected obtained and preset defect type detection model, determine described in wait for It detects target and whether there is defect, and when determining existing defects, before determining the corresponding defect type of the target to be detected, Further include:
Establish the process of the preset convolutional neural networks model, wherein the process includes:
Obtain initial convolutional neural networks model;
Obtain the bytecode of multiple sample objects, wherein the sample object is:It is compiled based on the default programming language The program write;
Based on the bytecode of each sample object, the corresponding sample color image of each sample object is obtained;
Obtain the corresponding calibration information of each sample color image, wherein the calibration information includes:Corresponding sample color Coloured picture is as corresponding defect type, as prediction defect type;
Based on being wrapped in each sample color image and the corresponding calibration information of each sample color image obtained The prediction defect type included, the training initial convolutional neural networks model, obtains preset convolutional neural networks model.
In one implementation, the initial convolutional neural networks model includes feature extraction layer and tagsort Layer;
It is described based in each sample color image obtained and the corresponding calibration information of each sample color image Included prediction defect type, the training initial convolutional neural networks model, obtains preset convolutional neural networks mould Type, including:
The each sample color image obtained is inputted into the feature extraction layer, extracts the sample of the sample color image Characteristics of image;
The sample image feature extracted is inputted into the tagsort layer, it is corresponding current to obtain the sample color image Defect type;
The current defect type obtained is matched with corresponding prediction defect type;
When successful match, the preset convolutional Neural net for including the feature extraction layer and the tagsort layer is obtained Network model;
When it fails to match, the parameter of the feature extraction layer and the tagsort layer is adjusted, return execution is described will The each sample color image obtained inputs the feature extraction layer, extracts the sample image feature of the sample color image;
Until when successful match, the preset convolutional Neural for including the feature extraction layer and the tagsort layer is obtained Network model.
In one implementation, described based on each sample color image obtained and each sample color figure As prediction defect type included in corresponding calibration information, the training initial convolutional neural networks model obtains pre- If convolutional neural networks model after, the method further includes:
Based on default testing process, the accuracy of the definitive result of the preset convolutional neural networks model is detected, is obtained Obtain testing result;
Obtain the adjust instruction for the testing result, wherein the adjust instruction carries required adjustment result;
Based on the adjustment obtained as a result, being adjusted to the preset convolutional neural networks model.
In one implementation, described to be detected based on the color image to be detected obtained and preset defect type Model determines that the target to be detected whether there is defect, and when determining existing defects, determines that the target to be detected corresponds to Defect type, including:
The color image to be detected is inputted into the preset convolutional neural networks model, the preset convolutional Neural Network model extracts image to be detected feature of the color image to be detected;And it is special based on the described image to be detected extracted Sign determines that the target to be detected whether there is defect, and when determining the target existing defects to be detected, is waited for described in determination Detect the corresponding defect type of target.
The bytecode for obtaining target to be detected, including:
It obtains and the detection that the target to be detected is detected is instructed, wherein the detection instruction includes described to be checked Survey the bytecode of target;
Based on the detection instruction obtained, the bytecode of the target to be detected is obtained.
The communication bus that above-mentioned electronic equipment is mentioned can be Peripheral Component Interconnect standard (Peripheral Component Interconnect, PCI) bus or expanding the industrial standard structure (Extended Industry Standard Architecture, EISA) bus etc..The communication bus can be divided into address bus, data/address bus, controlling bus etc..For just It is only indicated with a thick line in expression, figure, it is not intended that an only bus or a type of bus.
Communication interface is for the communication between above-mentioned electronic equipment and other equipment.
Memory may include random access memory (Random Access Memory, RAM), can also include non-easy The property lost memory (Non-Volatile Memory, NVM), for example, at least a magnetic disk storage.Optionally, memory may be used also To be at least one storage device for being located remotely from aforementioned processor.
Above-mentioned processor can be general processor, including central processing unit (Central Processing Unit, CPU), network processing unit (Network Processor, NP) etc.;It can also be digital signal processor (Digital Signal Processing, DSP), it is application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), existing It is field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete Door or transistor logic, discrete hardware components.
Corresponding to above method embodiment, the embodiment of the present invention additionally provides a kind of computer readable storage medium, described It is stored with computer program in computer readable storage medium, realizes that the present invention is real when the computer program is executed by processor Any of the above-described defect type detection method that example is provided is applied, which includes:
Obtain the bytecode of target to be detected, wherein the target to be detected is:It is write based on default programming language Program;
Based on the bytecode of the target to be detected obtained, the corresponding chromaticity diagram to be detected of the target to be detected is obtained Picture;
Based on the color image to be detected and preset defect type detection model obtained, the mesh to be detected is determined Mark whether there is defect, and when determining existing defects, determine the corresponding defect type of the target to be detected, wherein described Preset defect type detection model:Include the correspondence of each defect type and characteristics of image.
In the embodiment of the present invention, based on the bytecode of obtained target to be detected, the target to be detected of acquisition is corresponding to be waited for Detect color image, in turn, based on color image to be detected and preset defect type detection model included it is each lack Fall into the correspondence of type and characteristics of image, it may be determined that go out target to be detected and whether there is defect, and is to be detected determining When target existing defects, the corresponding defect type of target to be detected is determined, realize to target to be detected with the presence or absence of defect Detection is further realized when target existing defects to be detected, the detection to defect type.
In one implementation, the default programming language includes solidity language, and the target to be detected includes Intelligent contract.
In one implementation, the bytecode of the target to be detected is a string of character strings;
The bytecode based on the target to be detected obtained obtains the corresponding color to be detected of the target to be detected Image, including:
The bytecode of the target to be detected obtained is translated into RGB color generation based on the preset RGB color table of comparisons Code, wherein the preset RGB color table of comparisons includes:The correspondence of each RGB color code and character substring;
By the RGB color code, unloading is the corresponding color image to be detected of target to be detected.
In one implementation, the preset defect type detection model is preset convolutional neural networks model;
Described based on the color image to be detected obtained and preset defect type detection model, determine described in wait for It detects target and whether there is defect, and when determining existing defects, before determining the corresponding defect type of the target to be detected, Further include:
Establish the process of the preset convolutional neural networks model, wherein the process includes:
Obtain initial convolutional neural networks model;
Obtain the bytecode of multiple sample objects, wherein the sample object is:It is compiled based on the default programming language The program write;
Based on the bytecode of each sample object, the corresponding sample color image of each sample object is obtained;
Obtain the corresponding calibration information of each sample color image, wherein the calibration information includes:Corresponding sample color Coloured picture is as corresponding defect type, as prediction defect type;
Based on being wrapped in each sample color image and the corresponding calibration information of each sample color image obtained The prediction defect type included, the training initial convolutional neural networks model, obtains preset convolutional neural networks model.
In one implementation, the initial convolutional neural networks model includes feature extraction layer and tagsort Layer;
It is described based in each sample color image obtained and the corresponding calibration information of each sample color image Included prediction defect type, the training initial convolutional neural networks model, obtains preset convolutional neural networks mould Type, including:
The each sample color image obtained is inputted into the feature extraction layer, extracts the sample of the sample color image Characteristics of image;
The sample image feature extracted is inputted into the tagsort layer, it is corresponding current to obtain the sample color image Defect type;
The current defect type obtained is matched with corresponding prediction defect type;
When successful match, the preset convolutional Neural net for including the feature extraction layer and the tagsort layer is obtained Network model;
When it fails to match, the parameter of the feature extraction layer and the tagsort layer is adjusted, return execution is described will The each sample color image obtained inputs the feature extraction layer, extracts the sample image feature of the sample color image;
Until when successful match, the preset convolutional Neural for including the feature extraction layer and the tagsort layer is obtained Network model.
In one implementation, described based on each sample color image obtained and each sample color figure As prediction defect type included in corresponding calibration information, the training initial convolutional neural networks model obtains pre- If convolutional neural networks model after, the method further includes:
Based on default testing process, the accuracy of the definitive result of the preset convolutional neural networks model is detected, is obtained Obtain testing result;
Obtain the adjust instruction for the testing result, wherein the adjust instruction carries required adjustment result;
Based on the adjustment obtained as a result, being adjusted to the preset convolutional neural networks model.
Corresponding to above method embodiment, the embodiment of the present invention additionally provides a kind of computer program, wherein the calculating Any of the above-described defect type detection method that the embodiment of the present invention is provided is realized when machine program is executed by processor, it should Defect type detection method includes:
Obtain the bytecode of target to be detected, wherein the target to be detected is:It is write based on default programming language Program;
Based on the bytecode of the target to be detected obtained, the corresponding chromaticity diagram to be detected of the target to be detected is obtained Picture;
Based on the color image to be detected and preset defect type detection model obtained, the mesh to be detected is determined Mark whether there is defect, and when determining existing defects, determine the corresponding defect type of the target to be detected, wherein described Preset defect type detection model:Include the correspondence of each defect type and characteristics of image.
In the embodiment of the present invention, based on the bytecode of obtained target to be detected, the target to be detected of acquisition is corresponding to be waited for Detect color image, in turn, based on color image to be detected and preset defect type detection model included it is each lack Fall into the correspondence of type and characteristics of image, it may be determined that go out target to be detected and whether there is defect, and is to be detected determining When target existing defects, the corresponding defect type of target to be detected is determined, realize to target to be detected with the presence or absence of defect Detection is further realized when target existing defects to be detected, the detection to defect type.
In one implementation, the default programming language includes solidity language, and the target to be detected includes Intelligent contract.
In one implementation, the bytecode of the target to be detected is a string of character strings;
The bytecode based on the target to be detected obtained obtains the corresponding color to be detected of the target to be detected Image, including:
The bytecode of the target to be detected obtained is translated into RGB color generation based on the preset RGB color table of comparisons Code, wherein the preset RGB color table of comparisons includes:The correspondence of each RGB color code and character substring;
By the RGB color code, unloading is the corresponding color image to be detected of target to be detected.
In one implementation, the preset defect type detection model is preset convolutional neural networks model;
Described based on the color image to be detected obtained and preset defect type detection model, determine described in wait for It detects target and whether there is defect, and when determining existing defects, before determining the corresponding defect type of the target to be detected, Further include:
Establish the process of the preset convolutional neural networks model, wherein the process includes:
Obtain initial convolutional neural networks model;
Obtain the bytecode of multiple sample objects, wherein the sample object is:It is compiled based on the default programming language The program write;
Based on the bytecode of each sample object, the corresponding sample color image of each sample object is obtained;
Obtain the corresponding calibration information of each sample color image, wherein the calibration information includes:Corresponding sample color Coloured picture is as corresponding defect type, as prediction defect type;
Based on being wrapped in each sample color image and the corresponding calibration information of each sample color image obtained The prediction defect type included, the training initial convolutional neural networks model, obtains preset convolutional neural networks model.
In one implementation, the initial convolutional neural networks model includes feature extraction layer and tagsort Layer;
It is described based in each sample color image obtained and the corresponding calibration information of each sample color image Included prediction defect type, the training initial convolutional neural networks model, obtains preset convolutional neural networks mould Type, including:
The each sample color image obtained is inputted into the feature extraction layer, extracts the sample of the sample color image Characteristics of image;
The sample image feature extracted is inputted into the tagsort layer, it is corresponding current to obtain the sample color image Defect type;
The current defect type obtained is matched with corresponding prediction defect type;
When successful match, the preset convolutional Neural net for including the feature extraction layer and the tagsort layer is obtained Network model;
When it fails to match, the parameter of the feature extraction layer and the tagsort layer is adjusted, return execution is described will The each sample color image obtained inputs the feature extraction layer, extracts the sample image feature of the sample color image;
Until when successful match, the preset convolutional Neural for including the feature extraction layer and the tagsort layer is obtained Network model.
In one implementation, described based on each sample color image obtained and each sample color figure As prediction defect type included in corresponding calibration information, the training initial convolutional neural networks model obtains pre- If convolutional neural networks model after, the method further includes:
Based on default testing process, the accuracy of the definitive result of the preset convolutional neural networks model is detected, is obtained Obtain testing result;
Obtain the adjust instruction for the testing result, wherein the adjust instruction carries required adjustment result;
Based on the adjustment obtained as a result, being adjusted to the preset convolutional neural networks model.
It should be noted that herein, relational terms such as first and second and the like are used merely to a reality Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those Element, but also include other elements that are not explicitly listed, or further include for this process, method, article or equipment Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that There is also other identical elements in process, method, article or equipment including the element.
Each embodiment in this specification is all made of relevant mode and describes, identical similar portion between each embodiment Point just to refer each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality For applying example, since it is substantially similar to the method embodiment, so description is fairly simple, related place is referring to embodiment of the method Part explanation.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the scope of the present invention.It is all Any modification, equivalent replacement, improvement and so within the spirit and principles in the present invention, are all contained in protection scope of the present invention It is interior.

Claims (10)

1. a kind of defect type detection method, which is characterized in that the method includes:
Obtain the bytecode of target to be detected, wherein the target to be detected is:The journey write based on default programming language Sequence;
Based on the bytecode of the target to be detected obtained, the corresponding color image to be detected of the target to be detected is obtained;
Based on the color image to be detected and preset defect type detection model obtained, determine that the target to be detected is No existing defects, and when determining existing defects, determine the corresponding defect type of the target to be detected, wherein it is described default Defect type detection model:Include the correspondence of each defect type and characteristics of image.
2. according to the method described in claim 1, it is characterized in that, the default programming language includes solidity language, institute It includes intelligent contract to state target to be detected.
3. according to the method described in claim 1, it is characterized in that, the bytecode of the target to be detected is a string of character strings;
The bytecode based on the target to be detected obtained obtains the corresponding chromaticity diagram to be detected of the target to be detected Picture, including:
The bytecode of the target to be detected obtained is translated into RGB color code based on the preset RGB color table of comparisons, In, the preset RGB color table of comparisons includes:The correspondence of each RGB color code and character substring;
By the RGB color code, unloading is the corresponding color image to be detected of the target to be detected.
4. according to claim 1-3 any one of them methods, which is characterized in that the preset defect type detection model is Preset convolutional neural networks model;
Described based on the color image to be detected obtained and preset defect type detection model, determine described to be detected Target whether there is defect, and when determining existing defects, described before determining the corresponding defect type of the target to be detected Method further includes:
Establish the process of the preset convolutional neural networks model, wherein the process includes:
Obtain initial convolutional neural networks model;
Obtain the bytecode of multiple sample objects, wherein the sample object is:It is write based on the default programming language Program;
Based on the bytecode of each sample object, the corresponding sample color image of each sample object is obtained;
Obtain the corresponding calibration information of each sample color image, wherein the calibration information includes:Corresponding sample color figure As corresponding defect type, as prediction defect type;
Based on included in each sample color image and the corresponding calibration information of each sample color image obtained Predict that defect type, the training initial convolutional neural networks model obtain preset convolutional neural networks model.
5. according to the method described in claim 4, it is characterized in that, the initial convolutional neural networks model includes that feature carries Take layer and tagsort layer;
It is described based on being wrapped in each sample color image obtained and the corresponding calibration information of each sample color image The prediction defect type included, the training initial convolutional neural networks model, obtains preset convolutional neural networks model, wraps It includes:
The each sample color image obtained is inputted into the feature extraction layer, extracts the sample image of the sample color image Feature;
The sample image feature extracted is inputted into the tagsort layer, obtains the corresponding current defect of sample color image Type;
The current defect type obtained is matched with corresponding prediction defect type;
When successful match, the preset convolutional neural networks mould for including the feature extraction layer and the tagsort layer is obtained Type;
When it fails to match, the parameter of the feature extraction layer and the tagsort layer is adjusted, return execution is described to be obtained The each sample color image obtained inputs the feature extraction layer, extracts the sample image feature of the sample color image;
Until when successful match, the preset convolutional neural networks for including the feature extraction layer and the tagsort layer are obtained Model.
6. according to the method described in claim 4, it is characterized in that, it is described based on each sample color image obtained with And included prediction defect type in the corresponding calibration information of each sample color image, the training initial convolutional Neural Network model, after obtaining preset convolutional neural networks model, the method further includes:
Based on default testing process, the accuracy of the definitive result of the preset convolutional neural networks model is detected, is examined Survey result;
Obtain the adjust instruction for the testing result, wherein the adjust instruction carries required adjustment result;
Based on the adjustment obtained as a result, being adjusted to the preset convolutional neural networks model.
7. according to the method described in claim 4, it is characterized in that, described based on the color image to be detected obtained and pre- If defect type detection model, determine that the target to be detected whether there is defect, and when determining existing defects, determine institute The corresponding defect type of target to be detected is stated, including:
The color image to be detected is inputted into the preset convolutional neural networks model, the preset convolutional neural networks Image to be detected feature of color image to be detected described in model extraction;And based on image to be detected feature extracted, It determines that the target to be detected whether there is defect, and when determining the target existing defects to be detected, determines described to be checked Survey the corresponding defect type of target.
8. a kind of defect type detection device, which is characterized in that described device includes:
First obtains module, the bytecode for obtaining target to be detected, wherein the target to be detected is:Based on default volume The program that Cheng Yuyan is write;
Second obtains module, and for the bytecode based on the target to be detected obtained, it is corresponding to obtain the target to be detected Color image to be detected;
Determining module, for based on the color image to be detected and preset defect type detection model obtained, determining institute It states target to be detected and whether there is defect, and when determining existing defects, determine the corresponding defect type of the target to be detected, Wherein, the preset defect type detection model:Include the correspondence of each defect type and characteristics of image.
9. a kind of electronic equipment, which is characterized in that including processor, communication interface, memory and communication bus, wherein processing Device, communication interface, memory complete mutual communication by communication bus;
Memory, for storing computer program;
Processor when for executing the computer program stored on memory, realizes any defects of claim 1-7 Type detection method and step.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer in the computer readable storage medium Program realizes claim 1-7 any defect type detection method steps when the computer program is executed by processor Suddenly.
CN201810276600.9A 2018-03-30 2018-03-30 Defect type detection method, device, electronic equipment and medium Pending CN108509958A (en)

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