CN110097543A - Surfaces of Hot Rolled Strip defect inspection method based on production confrontation network - Google Patents

Surfaces of Hot Rolled Strip defect inspection method based on production confrontation network Download PDF

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CN110097543A
CN110097543A CN201910338448.7A CN201910338448A CN110097543A CN 110097543 A CN110097543 A CN 110097543A CN 201910338448 A CN201910338448 A CN 201910338448A CN 110097543 A CN110097543 A CN 110097543A
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徐林
田歌
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Northeastern University China
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Abstract

The present invention is a kind of Surfaces of Hot Rolled Strip defect inspection method based on production confrontation network, specific steps are as follows: (1) extract industry spot Surfaces of Hot Rolled Strip defect image, and carry out image preprocessing；(2) Maker model and arbiter model of building production confrontation network G AN.Specific practice is that conditional tag vector c is added in the input of generator, is used for output category image；Pixel loss L is introduced in generator trainingp, improve the quality for generating image；Setting differentiates branch and more classification branches in arbiter, realizes more classification features and improves nicety of grading；(3) it is optimized with production confrontation network parameter of the particle swarm algorithm PSO to construction；(4) image will be generated and true picture merges into Surfaces of Hot Rolled Strip defect sample collection.The present invention proposes that method is able to solve the problem of sample data deficiency, improves the speed and accuracy of defect image feature extraction, new effective ways are provided for Surfaces of Hot Rolled Strip defects detection.

Description

Surfaces of Hot Rolled Strip defect inspection method based on production confrontation network
Technical field
The invention belongs to Computer Vision Detection Technique fields, and in particular to a kind of hot rolling based on production confrontation network Steel strip surface defect detection method.
Technical background
Steel strip surface defect seriously affects the performances such as appearance, fatigue resistance, corrosion resistance and the wearability of steel product, shadow The subsequent use for ringing steel product, causes immeasurable industrial loss.Therefore the defects detection of belt steel product is in industrial production Extremely important step.Steel strip surface defect detection method is that machine detects from artificial development, improves speed and accuracy of identification. The current most common steel defect detection method is that defect characteristic is extracted and processed using means of different, then uses classifier pair Defect is classified.T Ojala in 2002 et al. describes a kind of classification method for being based on uniform local binary patterns (ULBP), This method obtains preferable performance at several aspects such as spatial resolution, grey scale change, rotation.Santanu in 2013 Ghorai et al. proposes a kind of Automatic Visual Inspection system with wavelet transform (DWT) feature and support vector machines (SVM) System.Northeastern University's Song Ke minister in 2013 etc. describe it is a kind of based on local binary patterns (LBP) being capable of effective antimierophonic band Steel defect identification method, mean accuracy 98%, and actual production can be effectively applied to.2016 Nian Wanglei et al. are adopted The feature vector that hot rolled steel plate surface image obtains higher-dimension is decomposed in Tetrole transformation, is sent into SVM after dimensionality reduction and is completed to hot-strip The Classification and Identification of surface defect, this method classification 8 kinds of defects such as including transversal crack, defect classification accuracy is 97.38%.
It is a kind of production model that production, which fights network (Generative Adversarial Network, GAN), Most basic application is exactly to model the distribution of truthful data and generate sample data, such as generate image and video etc..Inside GAN Using dual training mechanism, there is outstanding learning ability, sample encountered in conventional machines study can be effectively treated not The problem of foot, improves the speed and accuracy of feature extraction, new thinking is provided for Surfaces of Hot Rolled Strip defect recognition.
Summary of the invention
The purpose of the present invention is to propose to a kind of Surfaces of Hot Rolled Strip defect inspection methods based on production confrontation network, will GAN is applied to Surfaces of Hot Rolled Strip defects detection and is able to solve the problems such as data sample is few, degree of fitting is low, improves accuracy of identification.
The technical solution adopted by the present invention is that:
A kind of Surfaces of Hot Rolled Strip defect inspection method based on production confrontation network, includes the following steps:
Step 1: in conjunction with the case where industrial site, industry spot Surfaces of Hot Rolled Strip defect map is extracted in certain steel mill Picture, and pre-processed.
Step 2: the generation model and discrimination model of building production confrontation network G AN.
Following improve is made to the Maker model in production confrontation network.First, condition is added in generator input Label vector c, the i.e. input of generator are made of random noise z and conditional tag c.Second, picture is introduced in generator training Element loss Lp, improve the quality for generating image.Improve the network architecture of generator are as follows: input as by random vector and label The vector that vector connects and composes successively passes through convolutional layer Conv, active coating Relu, residual error structure Residual Block and warp Lamination Deconv.
Arbiter model in production confrontation network is made of differentiation branch and more classification branches.In sentencing for original GAN A conditional tag c is introduced in other device as branch is differentiated, increases the arbiter that branch of classifying one collectively forms GAN more.Its Middle to differentiate that branch is used to judge the image that input picture is true picture or is generated by generator, the effects for classifying branch have more Two, first is that the classification of input picture is judged, second is that generating Classification Loss Lcls.Differentiate the network mould of branch and more classification branches Type is similar, is made of convolutional layer Conv, active coating LeakyRelu and warp lamination Deconv.
Step 3: it is optimized with production confrontation network parameter of the particle swarm algorithm PSO to construction.
Production fights the training of network using alternating training method, and the training majorized function of GAN model is
Since the generator and arbiter structure of GAN model used are complex, propose a kind of based on particle swarm algorithm (PSO) production fights network optimized approach.Specific optimization process is using the parameter trained in model as in PSO algorithm One particle, then particle length n is exactly that trained number of parameters is participated in this network.Use loss function L as PSO algorithm Fitness function obtains the locally optimal solution and globally optimal solution of particle.Again with the position and speed of iterative manner more new particle, Obtained particle is exactly updated network weight, and with this, iteration continues, until adaptive value, i.e. training error, converge to threshold value The minimum of range completes the generator of production confrontation network and the parameter optimization of arbiter.
Step 4: using the true Surfaces of Hot Rolled Strip defect image of acquisition and conditional tag c as the input of generator G, Output image is mixed with true picture as Surfaces of Hot Rolled Strip defect sample collection.Take part of samples pictures as test specimens This collection, remaining is as training sample set.
Step 5: the arbiter D after extracting training removes the last layer, and carries out structure fine tuning, to hot-strip defect Sample image data is effectively identified and is classified.
Further, the pixel loss L introduced in generator trainingpThe clarity of generator output image can be increased, The recognition accuracy of Surfaces of Hot Rolled Strip defect is improved with the high pixel image processing of generation.Wherein
Lp=| G (z)-x |
The differentiation branch of arbiter is fed back in full articulamentum differentiates loss Ladv, it is true for characterizing the input of arbiter Image still generates image.And the full articulamentum output for branch of more classifying is N-dimensional vector, N is Surfaces of Hot Rolled Strip defective data Collect classification number, the purpose of training is the Classification Loss L of optimization feedbackcls.On differentiating that branch and more classification branches are shared when training One layer of weight differentiates the differentiation loss L of branchadvWith the Classification Loss L of more classification branch feedbacksclsCommon guidance generator is raw At the image of high quality.
To sum up, the loss function of GAN model is
L=Ladv+Lcls+Lp
The advantages of the present invention over the prior art are that:
1, the present invention constructs and production is trained to fight network G AN, can generate hot-strip defect image, enrich instruction The diversity and randomness for practicing sample set, have carried out effective enhancing to data sample, have solved Surfaces of Hot Rolled Strip defect sample Data scarcity problem, avoids over-fitting.
2, the present invention improves the generator and arbiter of original GAN, and pixel loss is introduced in generator, is drawn It leads and generates high-resolution defect image, replace low-quality image to carry out defect recognition with high quality graphic；It is same in arbiter When be added differentiate branch and classification branch, judge that image is true and false and carry out effective Classification and Identification, can be improved Surfaces of Hot Rolled Strip The average nicety of grading of defect, obtains preferable recognition effect.
3, the present invention optimizes the parameter of GAN model with particle swarm algorithm (PSO), and particle swarm algorithm has relatively strong Global optimizing ability, avoid occur in traditional optimal way gradient decline the problems such as, make production fight network acceleration Convergence improves training effect.
Detailed description of the invention
Fig. 1 is a kind of process of Surfaces of Hot Rolled Strip defect inspection method that network is fought based on production of the invention Figure.
Fig. 2 is the structural schematic diagram that production of the invention fights network.
Fig. 3 is the training process schematic diagram that production of the invention fights network.
Specific embodiment
The present invention is further described with reference to the accompanying drawings and detailed description.
It is as follows for implementation process of the invention as shown in Figure 1:
Step 1 acquires hot-strip defect image in industry spot, and does preliminary image preprocessing.
Step 2, building production confrontation network model simultaneously optimize, will treated true steel strip surface defect image and Conditional tag is input in model, observes the generation image of output.
Step 3 generates image and mixes with the Surfaces of Hot Rolled Strip defect image of acquisition, by image procossing as hot rolling Steel strip surface defect sample set.
Step 4 extracts discrimination model as classifier with particle swarm algorithm to GAN training is improved, adjustment parameter, effectively Identification hot-strip defect sample image data is simultaneously able to carry out classification.
It is illustrated in figure 2 the structural schematic diagram of production confrontation network of the invention.
Original GAN is made of generator G and arbiter D.Wherein, generator G is according to random noise generation and truthful data Similar puppet data, and arbiter D then judges that input is the pseudo- data of truthful data or generation.Due to the structure of original GAN Simply, control is difficult and training is too free, therefore introduces in generating model and discrimination model in the GAN model of this research Conditional tag c, contains defect classification information in the label, and the data of generator is instructed to generate and the identification of arbiter point Class.In arbiter model, arbiter is divided into two branches, respectively differentiation branch and more classification branches, realizes sentence respectively Other data are true and false and identify the function of classification.
It is illustrated in figure 3 the training process schematic diagram of production confrontation network of the invention.
The training optimization process of original GAN is exactly the minimax gambling process of generator G Yu arbiter D.Therefore model Optimization formula can be written as
Arbiter D is in order to better discriminate between the data that data are truthful datas or are generated by generator G, it is necessary to accurate Both identification simultaneously breaks up the probability as far as possible two that exports after judgement, i.e., so that D (x) is big as far as possible and D (G (x)) is small as far as possible；And it generates Device G then needs to be continuously improved generation data and arbiter cannot be distinguished in the similarity degree of truthful data.So two modules Training process be exactly the process vied each other, confronted with each other, the performance of the two is improved in constantly iteration, is finally reached Nash banlance (i.e. generation data identical as the differentiation probability of truthful data) into game theory, at this time arbiter D and generator G It is reached optimal.
During the improvement GAN actual optimization of this research, the training of the GAN method trained using substep alternating, specifically Training step are as follows:
(1) training generator network.By in the real defect data x mixing condition label c input generator of acquisition, generate High quality graphic.Calculate pixel loss Lp, pixel loss is bigger, illustrates that difference is bigger between high quality graphic and truthful data. The process of training generator is exactly to minimize pixel loss L with particle swarm algorithmp；
(2) parameter of fixed generator, with the differentiation branch for the high quality graphic sample training arbiter that generator generates With classification branch.
(3) the differentiation branch parameters of fixed arbiter, truthful data x are input in generator, outputting high quality image, The loss L of computational discrimination branchadv, feed back to generator and optimize the parameter of generator.
(4) the classification branch parameters of fixed arbiter, truthful data x are input in generator, outputting high quality image, Calculate the loss L of classification branchcls, feed back to generator and optimize the parameter of generator.
Repetition above-mentioned (2)~(4) step, repetitive exercise 10000 times.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited to above-mentioned Particular implementation, those skilled in the art can make a variety of changes or modify within the scope of the claims, this not shadow Ring substantive content of the invention.In the absence of conflict, the feature in embodiments herein and embodiment can any phase Mutually combination.

Claims (2)

1. a kind of Surfaces of Hot Rolled Strip defect inspection method based on production confrontation network, which is characterized in that including following step It is rapid:
Step 1: extracting industry spot Surfaces of Hot Rolled Strip defect image in conjunction with the case where industrial site in steel mill, going forward side by side Row pretreatment；
Step 2: the Maker model and arbiter model of building production confrontation network G AN；
Following improve is made to the Maker model in production confrontation network；
(1), conditional tag vector c is added in generator input, i.e., the input of generator is by random noise z and conditional tag c Composition；
(2), pixel loss L is introduced in generator trainingp, improve the quality for generating image；Improve the network model knot of generator Structure are as follows: input the vector to be connected and composed by random vector and label vector, successively by convolutional layer Conv, active coating Relu, Residual error structure Residual Block and warp lamination Deconv；
Following improve is made to the arbiter model in production confrontation network:
A conditional tag c is introduced in the arbiter of original GAN as branch is differentiated, increases the common structure of branch of classifying one more At the arbiter of GAN；Wherein differentiate branch be used to judge input picture be true picture or by generator generate image, it is more There are two the effects of classification branch, first is that the classification of input picture is judged, second is that generating Classification Loss Lcls；Differentiate branch and more The network model of classification branch is similar, is made of convolutional layer Conv, active coating LeakyRelu and warp lamination Deconv；
Step 3: being optimized with production confrontation network parameter of the particle swarm algorithm PSO to construction；
Production fights the training of network using alternating training method, and the training majorized function of GAN model is
Since the generator and arbiter structure of GAN model used are complex, a kind of life based on particle swarm algorithm PSO is proposed An accepted way of doing sth fights network optimized approach；Specific optimization process is the parameter that will train in model as a particle in PSO algorithm, Then particle length n is exactly that trained number of parameters is participated in this network；Use loss function L as the fitness letter of PSO algorithm Number, obtains the locally optimal solution and globally optimal solution of particle；Again with the position and speed of iterative manner more new particle, obtained grain Son is exactly updated network weight, and with this, iteration continues, until adaptive value, i.e. training error, converge to the pole of threshold range Small value, determines according to data sample, completes the generator of production confrontation network and the parameter optimization of arbiter；
Step 4: using the true Surfaces of Hot Rolled Strip defect image of acquisition and conditional tag c as the input of generator G, output Image is mixed with true picture as Surfaces of Hot Rolled Strip defect sample collection；Take part of samples pictures as test sample Collection, remaining is as training sample set；
Step 5: extracting the arbiter D after training, the last layer is removed, and carry out structure fine tuning, to hot-strip defect sample Image data is effectively identified and is classified.
2. a kind of Surfaces of Hot Rolled Strip defect inspection method based on production confrontation network according to claim 1, It is characterized in that, the pixel loss L introduced in generator trainingpThe clarity that generator output image can be increased, with generation The recognition accuracy of high pixel image processing raising Surfaces of Hot Rolled Strip defect；Wherein
Lp=| G (z)-x |
The differentiation branch of arbiter is fed back in full articulamentum differentiates loss Ladv, it is true picture for characterizing the input of arbiter Or generate image；And the full articulamentum output for branch of more classifying is N-dimensional vector, N is Surfaces of Hot Rolled Strip defective data collection class Shuo not；Its purpose trained is the Classification Loss L of optimization feedbackcls；Differentiate that branch and more classification branches share one layer when training Weight, differentiate branch differentiation loss LadvWith the Classification Loss L of more classification branch feedbacksclsCommon guidance generator generates high The image of quality；
To sum up, the loss function of GAN model are as follows:
L=Ladv+Lcls+Lp。
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