CN109142374A - Method and system based on the efficient Checking model of extra small sample training - Google Patents

Method and system based on the efficient Checking model of extra small sample training Download PDF

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CN109142374A
CN109142374A CN201810929603.8A CN201810929603A CN109142374A CN 109142374 A CN109142374 A CN 109142374A CN 201810929603 A CN201810929603 A CN 201810929603A CN 109142374 A CN109142374 A CN 109142374A
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罗晓忠
毛子靖
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Guangzhou Xinjian Zhikong Technology Co ltd
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Abstract

The present invention provides a kind of methods based on the efficient Checking model of extra small sample training, comprising: determines acquisition type, acquires small sample, small sample simulation is expanded；Random sampling upsets sequence；Squeezenet Pro trains Checking model；Test model result；Model is completed in publication.The present invention guarantees the deep learning model for achieving the effect that the training of hundreds of thousands sample number in the case where sample number is considerably less, to preferably be applied in Industrial Inferential Measurements.

Description

Method and system based on the efficient Checking model of extra small sample training
Technical field
The invention belongs to quality testing field, especially relate to a kind of based on the efficient Checking model of extra small sample training Method and system.
Background technique
Traditional vision algorithm is in the case where detecting slight flaws (1 the percent of flaw size about total size), to light The requirement of source camera is very harsh, and is directed to surface irregularity, and the product of color-variable needs to write a large amount of recognizer strategy To meet detection demand, this greatly reduces the development efficiency of quality detection project.
Since internet penetrates into the tempo increase of social every field, the reform and innovation of traditional industries are imperative, New technology is constantly used, make full use of internet technology, such as big data analysis, and intelligent algorithm is to reach quick, quasi- Really, easily product quality detects, and provides huge Data Detection analysis of data, accurately finds the multiclass of multi-product quality Type defect, the detection level of continuous upgrading products quality, upgrading synergy are the trend developed at present.
But in the industrial production, because sample number of faulty materials itself is less, and prepare a large amount of faulty materials and normal specimens Need to expend a large amount of manpowers, therefore offer large sample is almost impossible for big data analysis, the training of intelligent algorithm, together This condition of sample also limits the popularization of big data analysis, intelligent algorithm in industrial application.
Summary of the invention
In view of this, the present invention provides a kind of method and system based on the efficient Checking model of extra small sample training, guarantee In the case where sample number considerably less (more than ten of sample), achieve the effect that the deep learning model of hundreds of thousands sample number training, To preferably be applied in Industrial Inferential Measurements.
In order to achieve the above objectives, the technical scheme of the present invention is realized as follows:
A method of based on the efficient Checking model of extra small sample training, comprising:
Step 1: determining that acquisition type is accounted for including the faulty materials type and normal kind class to be detected according to flaw size The percentage of gross sample size is divided into N number of grade, acquires small sample；
Expand Step 2: the small sample of various types of, each grade is simulated；
Step 3: random sampling composition training set, verifying set, test set；Sequence is upset to each set；
Step 4: Squeezenet Pro trains Checking model；
Step 5: test model result；It is jumped in next step according to precision and testing time control；
Step 6: model is completed in publication, the test result of each flaw rank obtained according to step 5 provides every level-one The accuracy of identification of flaw, and the weight for freezing every level-one Squeezenet Pro is saved, issue corresponding Squeezenet Pro model.
Further, the particular content of step 1 includes:
1.1, the determination faulty materials type to be detected is K-1 times, >=2 K, and total classification is faulty materials species number+normal product Type=K class, every class sample collect Mk, k=1,2 ..., K small sample, it collects in totalA sample, wherein Mk ∈ [5,10]；
1.2, every class faulty materials are divided into N number of grade according to the percentage that flaw size accounts for gross sample size, wherein
1.3, every class sample is divided into two groups:WithWhereinForIn take out at random 80% sample taken is as training set；ForIn 20% sample randomly selected as test set.
Further, the particular content of step 2 includes:
2.1. manual analog sample；
By the kth class faulty materials of n-th of grade (n is bigger, and identification difficulty is higher) and last a kind of normal productK=1, 2 ..., K extends to the α of raw sample number by made mode1=5 times, i.e.,Faulty materials sum A at this time =α1M=5M；
2.2. it continuously takes pictures simulation；
By the kth class faulty materials of n-th of grade after manual simulation and last a kind of normal productExisted by unified samples Same light source Light0And camera Camera0Under the conditions of, with different angle Anglep, direction Directionp, position PositionpThe mode exptended sample number continuously taken pictures is to original α2=500 times,
Wherein Anglep∈ ANGLE, p=1,2 ..., P, ANGLE are the angles that product is likely to occur on actual production line Set；
Directionp∈ DIRECTION, p=1,2 ..., P, DIRECTION are that product may go out on actual production line Existing direction set；
Positionp∈ POSITION, p=1,2 ..., P, POSITION are that product is likely to occur on actual production line Location sets；
It is simulated by this, there is sample numberEvery class sample is divided into two groups: training setGather with test
To each b0∈B0Sample passes through camera Camera0It takes pictures, so that each b0Sample expands The noise that the photosensitive nuance of camera generates when the step by shooting every time carries out data extending, by continuously taking pictures The sample number obtained after simulation isIt obtains training setGather with test
2.3. automatic imitation sample；
Algorithm for image enhancement simulates β0Good sample again, image is rotated, is translated, is cut, is filled, brightness, comparison Degree, color difference operation, carry out sample number expansion for good sample in the case where not influencing product appearance structure and generating flaw, i.e.,
β is carried out to faulty materials using faulty materials simulation algorithm0Expansion again, to every a kind of faulty materialsK=1,2 ..., K-1 generates the image library of such faulty materials Mainly byInterception, the interconnection of flaw image section in faulty materials image The online image for searching for similar flaw part and the flaw image simulation based on dimensional gaussian distribution, wherein being based on dimensional Gaussian The flaw image simulation of distribution uses following formula W, h ∈ [- 2 γn, 2 γn], μ1, μ2∈ [-γn, γn], ρ ∈ (- 1,1), σ1, σ2∈ (0,2 γn],
Wherein γnReduce with the increase of n, indicates that the flaw of simulation becomes smaller, the difficulty of identification gradually rises；
To given flaw type k and given flaw grade n, random selectionFromMiddle random selection product imageAnd random selection coordinate (w0, h0), it willImage is placed in(w0, h0) at simulation generate new faulty materials, weight Multiple this process β0It is secondary, it obtains
To ownK=1,2 ..., K input generate in confrontation network DCGAN model, regenerate expansion and generate new sample This
The threedimensional model of product is got, and is based on threedimensional modelRandomly choose coordinate (w1, h1) textures extremely production In product threedimensional model, and light source Light is added in the three-dimensional model0And camera Camera0With different angle Anglep, direction Directionp, position (PositionpIt carries out simulation to take pictures, repeats this processIt is secondary, it obtains
The new data for summarizing all generations, obtains Wherein α3=β0+β1+β2,J=0,1,2；
To obtain training setGather with test
Further, the main contents of step 4 include:
4.1 Squeezenet Pro Model Weights copy: by the ownership from Conv1 layers to Conv10 layers when initialization Weight WI, I=1,2 ..., 10 is by Squeezenet V1.1 Model Weight WI(0) it is replaced；If had existed for The training pattern of Squeezenet Pro, then all weight WIIt is replaced by the weight of newest Squeezenet Pro；
The replacement of 4.2Squeezenet Pro Model Weight: the hidden neuron σ of the softmax layer of top is replaced with into K It is a, i.e.,
The W of 4.3 open Squeezenet ProiTraining is provided, frequency of training t is initializedn=0, j initial value are 10, input Data, using AdamGradient optimization algorithm training W10, and observational learning curve, work as DvalidOn test result reach Plateau marks leFor training precision, wherein e is epoch cycle of training, and mark ∈ is that training result minimum difference mark E is The minimum of cycle of training is online, when training precision differenceWhen, deconditioning judges le1It is It is no to be greater than 0.99, if it is, jumping to 4.4；If not i=i-1 is then arranged, the W of Squeezenet Pro is openediWeight Training is provided, 4.3 re -trainings are jumped to；
4.4 judge current frequency of training trainnWhether < Q is true, and wherein Q is total training time under n-th grade of flaw Number；If trainn< Q, then trainn=trainn+ 1, the training of screening again for the three carry out data that go to step；If trainn> Q, then go to step five.
Further, the main contents of step 5 include: by the test set of step 3 using newest trained Squeezenet Pro model is tested, if precision is more than or equal to 0.99, n=n+1 and increases flaw identification difficulty, and is jumped Go to simulation and training that step 2 carries out small sample；If precision less than 0.99, judges current testing time testn< O Whether true, wherein O is total testing time under n-th grade of flaw；If testn< O, then testn=testn+ 1, it jumps to Step 2 carries out the simulation and training of small sample, if testn> O, then go to step six.
Another aspect of the present invention additionally provides a kind of system based on the efficient Checking model of extra small sample training, comprising:
Acquisition module: acquisition type is determined, including the faulty materials type and normal kind class to be detected, according to flaw size The percentage for accounting for gross sample size is divided into N number of grade, acquires small sample；
Enlargement module: the small sample for various types of, each grade, which is simulated, to be expanded；
Decimation blocks: random sampling forms training set, verifying set, test set；Sequence is upset to each set；
Training module: Squeezenet Pro training Checking model；
Test module: test model result；It is jumped in next step according to precision and testing time control；
Release module: model is completed in publication, and the test result of each flaw rank obtained according to test module provides often The accuracy of identification of level-one flaw, and the weight for freezing every level-one Squeezenet Pro is saved, it issues corresponding Squeezenet Pro model.
Further, acquisition module includes:
Type unit: the determination faulty materials type to be detected is K-1 time, >=2 K, and total classification is faulty materials species number+just Normal kind class=K class, every class sample collect Mk, k=1,2 ..., K small sample, it collects in totalA sample, Wherein Mk∈ [5,10]；
Level cells: being divided into N number of grade according to the percentage that flaw size accounts for gross sample size for every class faulty materials, wherein
Grouped element: every class sample is divided into two groups:WithWhereinForIn 80% sample randomly selected is as training set；ForIn 20% sample randomly selected as test Set.
Further, enlargement module includes:
Manual analogue unit:
By the kth class faulty materials of n-th of grade (n is bigger, and identification difficulty is higher) and last a kind of normal productK=1, 2 ..., K extends to the α of raw sample number by made mode1=5 times, i.e.,Faulty materials sum A at this time =α1M=5M；
Continuous analogue unit of taking pictures:
By the kth class faulty materials of n-th of grade after manual simulation and last a kind of normal productExisted by unified samples Same light source Light0And camera Camera0Under the conditions of, with different angle Anglep, direction Directionp, position PositionpThe mode exptended sample number continuously taken pictures is to original α2=500 times,
Wherein Anglep∈ ANGLE, p=1,2 ..., P, ANGLE are the angles that product is likely to occur on actual production line Set；
Directionp∈ DIRECTION, p=1,2 ..., P, DIRECTION are that product may go out on actual production line Existing direction set；
Positionp∈ POSITION, p=1,2 ..., P, POSITION are that product is likely to occur on actual production line Location sets；
It is simulated by this, there is sample numberEvery class sample is divided into two groups: training setGather with test
To each b0∈B0Sample passes through camera Camera0It takes pictures, so that each b0Sample expands The noise that the photosensitive nuance of camera generates when the step by shooting every time carries out data extending, by continuously taking pictures The sample number obtained after simulation isIt obtains training setGather with test
Automatic imitation sample unit:
Algorithm for image enhancement simulates β0Good sample again, image is rotated, is translated, is cut, is filled, brightness, comparison Degree, color difference operation, carry out sample number expansion for good sample in the case where not influencing product appearance structure and generating flaw, i.e.,
β is carried out to faulty materials using faulty materials simulation algorithm0Expansion again, to every a kind of faulty materialsK=1,2 ..., K-1 generates the image library of such faulty materials Mainly byInterception, the interconnection of flaw image section in faulty materials image The online image for searching for similar flaw part and the flaw image simulation based on dimensional gaussian distribution, wherein being based on dimensional Gaussian The flaw image simulation of distribution uses following formula W, h ∈ [- 2 γn, 2 γn], μ1, μ2∈ [-γn, γn], ρ ∈ (- 1,1), σ1, σ2∈ (0,2 γn],
Wherein γnReduce with the increase of n, indicates that the flaw of simulation becomes smaller, the difficulty of identification gradually rises；
To given flaw type k and given flaw grade n, random selectionFromMiddle random selection product imageAnd random selection coordinate (w0, h0), it willImage is placed in(w0, h0) at simulation generate new faulty materials, weight Multiple this process β0It is secondary, it obtains
To ownK=1,2 ..., K input generates in confrontation network DCGAN model, and it is new to regenerate expansion generation Sample
The threedimensional model of product is got, and is based on threedimensional modelRandomly choose coordinate (w1, h1) textures extremely production In product threedimensional model, and light source Light is added in the three-dimensional model0And camera Camera0With different angle Anglep, direction Directionp, position (PositionpIt carries out simulation to take pictures, repeats this processIt is secondary, it obtains
The new data for summarizing all generations, obtains Wherein α3=β0+β1+β2,J=0,1,2；
To obtain training setGather with test
Further, training module includes:
Squeezenet Pro Model Weight copy cell: by the ownership from Conv1 layers to Conv10 layers when initialization Weight WI, I=1,2 ..., 10 is by Squeezenet V1.1 Model Weight WI(0) it is replaced；If had existed for The training pattern of Squeezenet Pro, then all weight WIIt is replaced by the weight of newest Squeezenet Pro；
Squeezenet Pro Model Weight replacement unit: the hidden neuron σ of the softmax layer of top is replaced with K, i.e.,
Training unit: the W of open Squeezenet ProiTraining is provided, frequency of training t is initializedn=0, j initial value is 10, input data, using AdamGradient optimization algorithm training W10, and observational learning curve, work as DvalidOn test result Reach Plateau, marks leFor training precision, wherein e is epoch cycle of training, and mark ∈ is training result minimum difference mark It is online to infuse the minimum that E is cycle of training, when training precision difference When, deconditioning, judgement le1Whether 0.99 is greater than, if it is, judging unit；If not i=i-1 is then arranged, the W of Squeezenet Pro is openedi Weight provides training, jumps to this training unit re -training；
Judging unit: judge current frequency of training trainnWhether < Q is true, and wherein Q is total instruction under n-th grade of flaw Practice number；If trainn< Q, then trainn=trainn+ 1, jump to the training of screening again that decimation blocks carry out data； If trainn> Q, then jump to test module.
Further, the main contents of test module include:
Precision judging unit: the test set that decimation blocks are obtained uses newest trained Squeezenet Pro model It is tested, if precision is more than or equal to 0.99, n=n+1 and increases flaw identification difficulty, and it is small to jump to enlargement module progress The simulation and training of sample；If precision turns frequency judging unit less than 0.99；
Frequency judging unit: judge current testing time testnWhether < O is true, and wherein O is under n-th grade of flaw Total testing time；If testn< O, then testn=testn+ 1, simulation and training that enlargement module carries out small sample are jumped to, If testn> O, then jump to release module.
Compared with prior art, the invention has the benefit that
1. use Conventional visual algorithm pretreatment image, prominent faulty materials and normal specimens difference, deep learning algorithm into Row classification realizes that stability of the variation more only with Conventional visual or deep learning algorithm to light is higher.If in Conventional visual There is subtle deviation in product polishing, can all impact to detection effect, needs to readjust parameter.And deep learning algorithm The polishing difference that can occur by model training, adaptive product, without readjusting to parameter, working service is convenient.
2. the top layer using SqueezeNet is revised as being adjusted, the faulty materials classification judged as needed is arranged to top The mode of the hidden neuron number of layer, it is stronger to the compatibility of multi-product, color.Conventional visual is for every a kind of faulty materials meeting Need special polishing scheme and criterion, system compatibility very poor with appearance characteristics such as color, sizes.And deep learning is calculated Method can be compatible with the detection demand of multi-product, different colours by the training to product model, and multi-product generates bumpless transfer.
3. using progressive algorithm, gradually training pattern makes deep learning model to product during training deep learning The detection of tiny flaw is more stable.Conventional visual scheme processing be less than sample size 1% nuance when, in order to noise Signal is mutually distinguished, and needs strictly to determine the features such as color, size, and smaller the spent debug time of flaw is longer, stability It is poor；The accuracy rate of identification is enhanced while reducing the development time using progressive algorithm.
4. making to need a large amount of training originally using Pro editions transfer training algorithms of small sample training algorithm+SqueezeNet The deep learning model of data only needs more than ten of training sample that training process can be completed, while can reduce out in training process The case where existing local optimum, accelerates model convergence rate.
Detailed description of the invention
Fig. 1: Conventional visual of embodiment of the present invention algorithm combines schematic diagram with deep learning algorithm；
Fig. 2: Small Sample Database of the embodiment of the present invention expands schematic diagram；
Fig. 3: SqueezeNet of embodiment of the present invention Pro editions application schematic diagrams；
Fig. 4: SqueezeNet of embodiment of the present invention Pro editions adjustable schematic diagrames；
Fig. 5: SqueezeNet of embodiment of the present invention Pro editions faulty materials classification training schematic diagrames；
Fig. 6: gradually training pattern schematic diagram of the embodiment of the present invention.
Specific embodiment
It should be noted that in the absence of conflict, the feature in the embodiment of the present invention and embodiment can be mutual Combination.
The invention proposes the deep learning model algorithms based on small sample training, pass through this method, it is ensured that In the case where sample number considerably less (more than ten of sample), achieve the effect that the deep learning model of hundreds of thousands sample number training, from And preferably it is applied in Industrial Inferential Measurements.
Step 1: acquisition small sample
1.1. the determination faulty materials type to be detected is K-1 times, >=2 K, and total classification is faulty materials species number+normal product Type=K class, every class sample collect Mk, k=1,2 ..., K small sample, it collects in totalA sample, wherein one As in the case of Mk∈ [5,10].
1.2. every class faulty materials are divided into N number of grade according to the percentage that flaw size accounts for gross sample size, wherein
1.3. every class sample is divided into two groups:WithWhereinForIn it is random 80% sample extracted is as training set (Training Set)；ForIn 20% sample randomly selected Product are as test set (Testing Set).
Step 2: small sample expands
2.1. manual analog sample
2.1.1. by the kth class faulty materials of n-th of grade (n is bigger, and identification difficulty is higher) and last a kind of normal productk =1,2 ..., K extends to the α of raw sample number by made mode1=5 times, i.e.,Faulty materials are total at this time Number A=α1M=5M.
2.2. it continuously takes pictures simulation
2.2.1. by the kth class faulty materials of n-th of grade after manual simulation and last a kind of normal productPass through unification Sample is in same light source (Light0) and camera (Camera0) under the conditions of, with different angle (Anglep), direction (Directionp), position (Positionp) the mode exptended sample number continuously taken pictures is to original α2=500 times, wherein Anglep∈ ANGLE, p=1,2 ..., P, ANGLE are the angle sets that product is likely to occur on actual production line,；Similarly There is Directionp∈ DIRECTION, p=1,2 ..., P, DIRECTION are the sides that product is likely to occur on actual production line To set；Positionp∈ POSITION, p=1,2 ..., P, POSITION are that product is likely to occur on actual production line Location sets.It is simulated by this, there is sample numberLikewise, havingFor based onThe training set (Training Set) of generation；For based onThe test set of generation (Testing Set)。
2.2.2. to each b0∈B0Sample passes through camera Camera0It takes pictures, so that each b0Sample expands The noise that the photosensitive nuance of camera generates when the step by shooting every time carries out data extending, can be very The process of effective simulation actual production line detection, so that deep learning be allowed to train the model of stable anti-noise.By continuously clapping It is according to the sample number obtained after simulationLikewise, havingFor based onIt generates Training set (Training Set)；For based onThe test set (Testing Set) of generation.
2.3. automatic imitation sample
2.3.1. algorithm for image enhancement simulates β0Good sample (β again0It usually takes 10), image is rotated, translate, is cut out It cuts, fill, brightness, contrast, the operation such as color difference, in the case where not influencing product appearance structure and generating flaw by good sample Sample number expansion is carried out, i.e.,
2.3.2. β is carried out to faulty materials using faulty materials simulation algorithm0Expansion again, to every a kind of faulty materialsK=1, 2 ..., K-1 generates the image library of such faulty materials Mainly bySection of flaw image section in faulty materials image It takes, search for the image of similar flaw part and the flaw image simulation based on dimensional gaussian distribution on internet, wherein being based on two The flaw image simulation for tieing up Gaussian Profile uses following formula W, h ∈ [- 2 γn, 2 γn], μ1, μ2∈ [-γn, γn], ρ ∈ (- 1,1), σ1, σ2∈ (0,2 γn], wherein γnIt can reduce with the increase of n, indicate the flaw of simulation Become smaller, the difficulty of identification gradually rises.To given flaw type k and given flaw grade n, random selection FromIn Randomly choose product imageAnd random selection coordinate (w0, h0), it willImage is placed in(w0, h0) at simulate life The faulty materials of Cheng Xin repeat this process β0It is secondary, therefore obtain
2.3.3. will ownK=1,2 ..., K input generate in confrontation network (DCGAN) model, will regenerate expansion Fill generation new samples
2.3.4. the threedimensional model of product is got, and provided on threedimensional model based on 2.3.2Random selection is sat Mark (w1, h1) textures are into product threedimensional model, and addition light source (Light in the three-dimensional model0) and camera (Camera0) with Different angle (Anglep), direction (Directionp), position (Positionp) simulate and take pictures, repeat this process It is secondary, therefore obtain
2.3.5. the new data for summarizing all generations, obtains Wherein α3=β0+β1+β2,J=0,1,2.
Note: in this algorithm, αi, i=1,2,3 can be greater than replaced 0 natural number by any.Likewise, havingFor based onThe training set (Training Set) of generation；For based on The test set (Testing Set) of generation.
Step 3: random sampling forms training set
3.1. to given flaw type k and given flaw grade n, fromIn randomly select 50% data make For training set Dtrain, 10% data are as verifying set Dvalid；FromIn randomly select 50% data as surveying Examination collection Dtest.Sequence is upset to each D set, enters step four.
Step 4: Squeezenet Pro trains Checking model
4.1 Squeezenet Pro Model Weights copy: by the ownership from Conv1 layers to Conv10 layers when initialization Weight WI, I=1,2 ..., 10 is by Squeezenet V1.1 Model Weight WI(0) it is replaced.If had existed for The training pattern of Squeezenet Pro, then all weight WIIt is replaced by the weight of newest Squeezenet Pro.
The replacement of 4.2 Squeezenet Pro Model Weights: the hidden neuron σ of the softmax layer of top is replaced with K, i.e.,
The W of 4.3 open Squeezenet ProiTraining is provided, frequency of training t is initializedn=0, j initial value are 10, input number According to using AdamGradient optimization algorithm training W10, and observational learning curve, work as DvalidOn test result reach Plateau, Mark leFor training precision, wherein e is cycle of training (epoch), and mark ∈ is training result minimum difference, usually 0.5%, it marks It is online to infuse the minimum that E is cycle of training, usually 50, have when training precision differenceWhen, stop Training, judges le1Whether 0.99 is greater than, if it is, jumping to
4.4；If not i=i-1 is then arranged, the W of Squeezenet Pro is openediWeight provides training, jumps to 4.3 Re -training.
4.4 judge current frequency of training trainnWhether < Q is true, and wherein Q is total training time under n-th grade of flaw Number, general Q=5.If trainn< Q, then trainn=trainn+ 1, jump to the training of screening again of 3.1 carry out data. If trainn> Q, then go to step five.
Step 5: test model result
5.1. by DtestIt is tested using newest trained Squeezenet Pro model, if precision is more than or equal to 0.99, then n=n+1 increases flaw identification difficulty, and jumps to the simulation and training of 2.1 carry out small samples.If precision is less than 0.99, then judge current testing time testnWhether < O is true, and wherein O is total testing time under n-th grade of flaw, generally O=3.If testn< O, then testn=testn+ 1, jump to the simulation and training of 2.1 carry out small samples.If testn> O, then go to step six.
Step 6: model is completed in publication
The test result of each flaw rank obtained according to step 5 provides the accuracy of identification of every level-one flaw, and protects Deposit the weight for freezing every level-one Squeezenet ProIssue corresponding Squeezenet Pro model.
The features of the present invention is as follows:
It is detected as shown in Figure 1, Conventional visual algorithm combines common product quality of completing with deep learning algorithm.Tradition view Feel algorithm pretreatment image, prominent faulty materials and normal specimens difference, deep learning algorithm are classified；
As shown in Fig. 2, after Small Sample Database of the invention provides, by continuously taking pictures, simulating bad sample, automatic mold manually Intend bad sample, threedimensional model generates bad sample, generate confrontation network generate bad sample method carry out data extending, data are expanded Fill 100,000 times；
To be applied in product quality detection as shown in figure 3, present invention firstly provides SqueezeNet Pro editions, guarantee algorithm Precision while also faster than the speed of other models；
As shown in figure 4, the top layer of SqueezeNet Pro editions more traditional SqueezeNet is revised as being adjusted in the present invention, The faulty materials classification judged as needed is arranged to the hidden neuron number of top layer；
As shown in figure 5, SqueezeNet Pro editions of the present invention all layers of the parameters other than top layer by training in advance The parameter of 1000 class image recognitions is substituted, and is trained by the algorithm of transfer learning to faulty materials classification；
As shown in fig. 6, pass through progressive algorithm gradually training pattern in training process of the present invention, it now will most apparent faulty materials By being trained after data extending algorithm, it is being stepped up the training for identifying that difficulty finally realizes all faulty materials types.
The foregoing describe the information such as basic principles and main features of the invention and embodiment, but the present invention is not by upper The limitation for stating implementation process, under the premise of not departing from spirit and range, the present invention can also have various changes and modifications. Therefore, unless this changes and improvements are departing from the scope of the present invention, they should be counted as comprising in the present invention.

Claims (10)

1. a kind of method based on the efficient Checking model of extra small sample training characterized by comprising
Step 1: determining that acquisition type accounts for gross sample according to flaw size including the faulty materials type and normal kind class to be detected The percentage of product size is divided into N number of grade, acquires small sample；
Expand Step 2: the small sample of various types of, each grade is simulated；
Step 3: random sampling composition training set, verifying set, test set；Sequence is upset to each set；
Step 4: Squeezenet Pro trains Checking model；
Step 5: test model result；It is jumped in next step according to precision and testing time control；
Step 6: model is completed in publication, the test result of each flaw rank obtained according to step 5 provides every level-one flaw Accuracy of identification, and save and freeze the weight of every level-one Squeezenet Pro, issue corresponding Squeezenet Pro mould Type.
2. the method according to claim 1, wherein the particular content of step 1 includes:
1.1, the determination faulty materials type to be detected is K-1 times, >=2 K, and total classification is faulty materials species number+normal kind class =K class, every class sample collect Mk, k=1,2 ..., K small sample, it collects in totalA sample, wherein Mk∈ [5,10]；
1.2, every class faulty materials are divided into N number of grade according to the percentage that flaw size accounts for gross sample size, wherein
1.3, every class sample is divided into two groups:WithWhereinForIn randomly select 80% sample is as training set；ForIn 20% sample randomly selected as test set.
3. the method according to claim 1, wherein the particular content of step 2 includes:
2.1. manual analog sample；
By the kth class faulty materials of n-th of grade (n is bigger, and identification difficulty is higher) and last a kind of normal product The α of raw sample number is extended to by made mode1=5 times, i.e.,Faulty materials sum A=α at this time1M= 5M；
2.2. it continuously takes pictures simulation；
By the kth class faulty materials of n-th of grade after manual simulation and last a kind of normal productBy unified samples same Light source Light0And camera Camera0Under the conditions of, with different angle Anglep, direction Directionp, position PositionpEven The continuous mode exptended sample number taken pictures is to original α2=500 times,
Wherein Anglep∈ ANGLE, p=1,2 ..., P, ANGLE are the angle sets that product is likely to occur on actual production line；
Directionp∈ DIRECTION, p=1,2 ..., P, DIRECTION are the sides that product is likely to occur on actual production line To set；
Positionp∈ POSITION, p=1,2 ..., P, POSITION are the positions that product is likely to occur on actual production line Set；
It is simulated by this, there is sample numberEvery class sample is divided into two groups: training setGather with test
To each b0∈B0Sample passes through camera Camera0It takes pictures, so that each b0Sample expands It should The noise that the photosensitive nuance of camera generates when step by shooting every time carries out data extending, after simulation of continuously taking pictures Obtained sample number isIt obtains training setGather with test
2.3. automatic imitation sample；
Algorithm for image enhancement simulates β0Image is rotated, is translated, being cut, being filled, brightness, contrast, color difference behaviour by good sample again Make, good sample is subjected to sample number expansion in the case where not influencing product appearance structure and generating flaw, i.e.,
β is carried out to faulty materials using faulty materials simulation algorithm0Expansion again, to every a kind of faulty materials Generate the image library of such faulty materials Mainly byIn the interception of flaw image section in faulty materials image, internet The image of similar flaw part and the flaw image simulation based on dimensional gaussian distribution are searched for, wherein being based on dimensional gaussian distribution Flaw image simulation use following formula
Wherein γnReduce with the increase of n, indicates that the flaw of simulation becomes smaller, the difficulty of identification gradually rises；
To given flaw type k and given flaw grade n, random selectionFromMiddle random selection product imageAnd random selection coordinate (w0, h0), it willImage is placed in(w0, h0) at simulation generate new faulty materials, weight Multiple this process β0It is secondary, it obtains
To ownInput generates in confrontation network DCGAN model, regenerates expansion and generates new samples
The threedimensional model of product is got, and is based on threedimensional modelRandomly choose coordinate (w1, h1) textures to product three-dimensional In model, and light source Light is added in the three-dimensional model0And camera Camera0With different angle Anglep, direction Directionp, position (PositionpIt carries out simulation to take pictures, repeats this processIt is secondary, it obtains
The new data for summarizing all generations, obtains Wherein
To obtain training setGather with test
4. the method according to claim 1, wherein the main contents of step 4 include:
4.1 Squeezenet Pro Model Weights copy: by all weight W from Conv1 layers to Conv10 layers when initializationI, I =1,2 ..., 10 by Squeezenet V1.1 Model Weight WI(0) it is replaced；If having existed for Squeezenet Pro Training pattern, then all weight WIIt is replaced by the weight of newest Squeezenet Pro；
The replacement of 4.2 Squeezenet Pro Model Weights: replacing with K for the hidden neuron σ of the softmax layer of top, I.e.
The W of 4.3 open Squeezenet ProiTraining is provided, frequency of training t is initializedn=0, j initial value are 10, input number According to using AdamGradient optimization algorithm training W10, and observational learning curve, work as DvalidOn test result reach Plateau marks leFor training precision, wherein e is epoch cycle of training, and mark ∈ is that training result minimum difference mark E is The minimum of cycle of training is online, when training precision differenceWhen, deconditioning judges le1Whether Greater than 0.99, if it is, jumping to 4.4；If not i=i-1 is then arranged, the W of Squeezenet Pro is openediWeight mentions For training, 4.3 re -trainings are jumped to；
4.4 judge current frequency of training trainnWhether < Q is true, and wherein Q is total frequency of training under n-th grade of flaw；If trainn< Q, then trainn=trainn+ 1, the training of screening again for the three carry out data that go to step；If trainn> Q, Then go to step five.
5. the method according to claim 1, wherein the main contents of step 5 include: by the test of step 3 Collection is tested using newest trained Squeezenet Pro model, if precision increases more than or equal to 0.99, n=n+1 Flaw is added to identify difficulty, and the simulation and training for the two carry out small samples that go to step；If precision, less than 0.99, judgement is worked as Preceding testing time testnWhether < O is true, and wherein O is total testing time under n-th grade of flaw；If testn< O, then testn=testn+ 1, the simulation and training of the two carry out small samples that go to step, if testn> O, then go to step six.
6. a kind of system based on the efficient Checking model of extra small sample training characterized by comprising
Acquisition module: determine that acquisition type accounts for always including the faulty materials type and normal kind class to be detected according to flaw size The percentage of sample size is divided into N number of grade, acquires small sample；
Enlargement module: the small sample for various types of, each grade, which is simulated, to be expanded；
Decimation blocks: random sampling forms training set, verifying set, test set；Sequence is upset to each set；
Training module: Squeezenet Pro training Checking model；
Test module: test model result；It is jumped in next step according to precision and testing time control；
Release module: model is completed in publication, and the test result of each flaw rank obtained according to test module provides every level-one The accuracy of identification of flaw, and the weight for freezing every level-one Squeezenet Pro is saved, issue corresponding Squeezenet Pro model.
7. system according to claim 6, which is characterized in that acquisition module includes:
Type unit: the determination faulty materials type to be detected is K-1 times, >=2 K, and total classification is faulty materials species number+normal product Type=K class, every class sample collect Mk, k=1,2 ..., K small sample, it collects in totalA sample, wherein Mk ∈ [5,10]；
Level cells: being divided into N number of grade according to the percentage that flaw size accounts for gross sample size for every class faulty materials, wherein
Grouped element: every class sample is divided into two groups:WithWhereinForIn it is random 80% sample extracted is as training set；ForIn 20% sample randomly selected as test set.
8. system according to claim 6, which is characterized in that enlargement module includes:
Manual analogue unit:
By the kth class faulty materials of n-th of grade (n is bigger, and identification difficulty is higher) and last a kind of normal product The α of raw sample number is extended to by made mode1=5 times, i.e.,Faulty materials sum A=α at this time1M= 5M；
Continuous analogue unit of taking pictures:
By the kth class faulty materials of n-th of grade after manual simulation and last a kind of normal productBy unified samples same Light source Light0And camera Camera0Under the conditions of, with different angle Anglep, direction Directionp, position PositionpEven The continuous mode exptended sample number taken pictures is to original α2=500 times,
Wherein Anglep∈ ANGLE, p=1,2 ..., P, ANGLE are the angle sets that product is likely to occur on actual production line；
Directionp∈ DIRECTION, p=1,2 ..., P, DIRECTION are the sides that product is likely to occur on actual production line To set；
Positionp∈ POSITION, p=1,2 ..., P, POSITION are the positions that product is likely to occur on actual production line Set；
It is simulated by this, there is sample numberEvery class sample is divided into two groups: training setGather with test
To each b0∈B0Sample passes through camera Camera0It takes pictures, so that each b0Sample expands It should The noise that the photosensitive nuance of camera generates when step by shooting every time carries out data extending, after simulation of continuously taking pictures Obtained sample number isIt obtains training setGather with test
Automatic imitation sample unit:
Algorithm for image enhancement simulates β0Good sample again, image is rotated, is translated, is cut, is filled, brightness, contrast, color Difference operation, carries out sample number expansion for good sample in the case where not influencing product appearance structure and generating flaw, i.e.,
β is carried out to faulty materials using faulty materials simulation algorithm0Expansion again, to every a kind of faulty materials Generate the image library of such faulty materials Mainly byIn the interception of flaw image section in faulty materials image, internet The image of similar flaw part and the flaw image simulation based on dimensional gaussian distribution are searched for, wherein being based on dimensional gaussian distribution Flaw image simulation use following formula
Wherein γnReduce with the increase of n, indicates that the flaw of simulation becomes smaller, the difficulty of identification gradually rises；
To given flaw type k and given flaw grade n, random selectionFromMiddle random selection product imageAnd random selection coordinate (w0, h0), it willImage is placed in(w0, h0) at simulation generate new faulty materials, weight Multiple this process β0It is secondary, it obtains
To ownInput generates in confrontation network DCGAN model, regenerates expansion and generates new samples
The threedimensional model of product is got, and is based on threedimensional modelRandomly choose coordinate (w1, h1) textures to product three-dimensional In model, and light source Light is added in the three-dimensional model0And camera Camera0With different angle Anglep, direction Directionp, position (PositionpIt carries out simulation to take pictures, repeats this processIt is secondary, it obtains
The new data for summarizing all generations, obtains Wherein
To obtain training setGather with test
9. system according to claim 6, which is characterized in that training module includes:
Squeezenet Pro Model Weight copy cell: by all weight W from Conv1 layers to Conv10 layers when initializationI, I =1,2 ..., 10 by Squeezenet V1.1 Model Weight WI(0) it is replaced；If having existed for Squeezenet Pro Training pattern, then all weight WIIt is replaced by the weight of newest Squeezenet Pro；
Squeezenet Pro Model Weight replacement unit: replacing with K for the hidden neuron σ of the softmax layer of top, I.e.
Training unit: the W of open Squeezenet ProiTraining is provided, frequency of training t is initializedn=0, j initial value are 10, defeated Enter data, using AdamGradient optimization algorithm training W10, and observational learning curve, work as DvalidOn test result reach Plateau marks leFor training precision, wherein e is epoch cycle of training, and mark ∈ is that training result minimum difference mark E is The minimum of cycle of training is online, when training precision difference When, deconditioning judges le1Whether Greater than 0.99, if it is, judging unit；If not i=i-1 is then arranged, the W of Squeezenet Pro is openediWeight mentions For training, this training unit re -training is jumped to；
Judging unit: judge current frequency of training trainnWhether < Q is true, and wherein Q is total training time under n-th grade of flaw Number；If trainn< Q, then trainn=trainn+ 1, jump to the training of screening again that decimation blocks carry out data；If trainn> Q, then jump to test module.
10. system according to claim 6, which is characterized in that the main contents of test module include:
Precision judging unit: the test set that decimation blocks are obtained is carried out using newest trained Squeezenet Pro model Test if precision is more than or equal to 0.99, n=n+1 and increases flaw identification difficulty, and jumps to enlargement module and carries out small sample Simulation and training；If precision turns frequency judging unit less than 0.99；
Frequency judging unit: judge current testing time testnWhether < O is true, and wherein O is total test under n-th grade of flaw Number；If testn< O, then testn=testn+ 1, simulation and training that enlargement module carries out small sample are jumped to, if testn> O, then jump to release module.
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Application publication date: 20190104

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