CN112164040A - Steel surface defect identification method based on semi-supervised deep learning algorithm - Google Patents

Steel surface defect identification method based on semi-supervised deep learning algorithm Download PDF

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CN112164040A
CN112164040A CN202010982319.4A CN202010982319A CN112164040A CN 112164040 A CN112164040 A CN 112164040A CN 202010982319 A CN202010982319 A CN 202010982319A CN 112164040 A CN112164040 A CN 112164040A
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CN112164040B (en
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王洪成
郑小青
孔亚广
郑松
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Hangzhou Dianzi University
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Abstract

The invention relates to the technical field of image classification and identification, in particular to a steel surface defect identification method based on a semi-supervised deep learning algorithm, which comprises the following steps: A) acquiring an image of the surface of the steel and obtaining initial image sample data; B) dividing initial image sample data into a training data set and a verification data set according to a preset proportion, selecting 20% of the training data set to perform manual marking to serve as a labeled sample set, and taking the rest as a non-labeled sample set; C) inputting the training data set in the step B) into a model classifier for iterative training, and verifying by using samples in a verification sample set in each iteration N steps until a preset threshold value is reached; D) and inputting the new steel surface image into the model classifier to obtain a defect identification result of the steel surface image. The substantial effects of the invention are as follows: the neural network model can adapt to sample data without labels, the sample data with labels required by the classifier is greatly reduced, and the training cost of the classifier is reduced.

Description

Steel surface defect identification method based on semi-supervised deep learning algorithm
Technical Field
The invention relates to the technical field of image classification and identification, in particular to a steel surface defect identification method based on a semi-supervised deep learning algorithm.
Background
The automatic identification of the steel surface defects based on deep learning is one of research hotspots in steel quality inspection, but most of the current deep learning quality inspection methods focus on a supervised learning algorithm which needs large-scale marking samples. In some practical cases, it is difficult to collect and mark enough data samples for the supervised learning algorithm model, so that the application and development of deep learning in the field of steel surface quality inspection are influenced. For example, chinese patent CN109242825A, published 2019, 1 month, 18 days, a method and an apparatus for identifying defects on a steel surface based on a deep learning technique, the method includes: collecting corresponding steel surface defect pictures according to the type of the steel production line; marking the defects according to a marking process of a marking platform to generate a training sample set and a verification sample set; designing a neural network model according to the types and characteristics of the steel surface defects; training a neural network model according to the training sample set and the training process of the neural network; evaluating each index of the neural network model according to the calibration sample set and the evaluation method of the neural network model, and optimizing the neural network model; and according to the equipment performance and the size of the neural network model, carrying out the deployment and application of the neural network model in a distributed mode. The technical scheme adopts the fully labeled samples to train the classification model, and the efficiency is low. The semi-supervised deep learning utilizes marked samples and unmarked samples to carry out model training, and can well overcome the problem that a large number of marked samples are difficult to obtain. Therefore, it is necessary to develop a classification model construction technique using semi-supervised learning.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the defect identification is inaccurate in calculation due to insufficient labeled sample data in the existing steel surface defect machine identification. The method can reduce the number of labeled samples required by classification model training, fully utilizes sample data without labels, and reduces the difficulty of classification model establishment.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: the steel surface defect identification method based on the semi-supervised deep learning algorithm comprises the following steps: A) acquiring an image of the surface of the steel and obtaining initial image sample data; B) dividing initial image sample data into a training data set and a verification data set according to a preset proportion, turning and shearing the training data set for enhancement, selecting 20% of the training data set for manual marking to serve as a labeled sample set, and taking the rest as a non-labeled sample set to add labels to the verification data set; C) inputting the training data set in the step B) into a model classifier for iterative training, verifying by using samples in a verification sample set in every iteration N steps, and recording the accuracy of the model classifier until the accuracy reaches a preset threshold; D) inputting the new steel surface image into the model classifier trained in the step C) to obtain a defect identification result of the steel surface image. The number of samples is increased by turning and shearing the training data set, and manual marking is performed by selecting 20%, so that the cost of sample marking is reduced.
Preferably, in the step a), the steel surface image is preprocessed: A1) carrying out normalization processing on the steel surface image; A2) and scaling the normalized steel surface image to enable the pixel width of the shorter side of the normalized steel surface image to be a preset value m, and then cutting the longer side of the normalized steel surface image to enable the steel surface image to be converted into a format of m multiplied by 3. The normalized steel surface image is easier to be processed by a classifier. Wherein m is preferably 32x32, the preferred reason is the pixel size of the steel surface defect, which can be generally shown by 32x32 pixel size, and the meaningless data required to be processed by the classifier is reduced.
Preferably, in step C), the model classifier is a neural network, and the method for iteratively training the neural network includes: C1) setting a threshold ηt1、ηt2And ηt3(ii) a C2) Taking a sample x from a labeled sample set or an unlabeled sample set1Input to the neural network to obtain an output result f of the neural networkθ(x1)，fθ(x1) Including the probability of k defects, if sample x1Belongs to a set of labeled samples fθ(x1)|k≤ηt1Then sample x is sampled1Is the neural network f, is the loss function L, thetaθ(x) If the sample x is the training parameter of1Belongs to a set of unlabeled samples, fθ(x2)|k<ηt2And f isθ(x2)|k<ηt3Then sample x is sampled2The training result of (a) is incorporated into the loss function L; C3) calculating the gradient g from the loss function Lθ，
Figure BDA0002688004700000021
Updating a sliding average parameter theta', theta ═ theta +(1-alpha) theta, alpha is a sliding average rate, and then calculating the neural network fθ(x) Is equal to Step (theta', g)θ) The number of iterations t is added to 1, and the process is re-executed from step C1). Whether the loss function L is included is improved, so that the neural network model can adapt to sample data without labels.
Preferably, in step C2), sample x is obtained1The pathway of (a) further comprises: taking two samples x simultaneously from a set of labeled or unlabeled samplesjAnd xl(ii) a Two samples xjAnd xlMixing is carried out xm＝Mixλ(xj,xl)＝λxj+(1-λ)xlAnd the label is mixed by interpolation,
Figure BDA0002688004700000022
Figure BDA0002688004700000023
will be provided with
Figure BDA0002688004700000024
As a sample x1And inputting the neural network. The number of samples can be increased through sample mixing, the existing labeled sample data is fully used, and the sample data required by training of the neural network model is reduced.
Preferably, step C2)Obtaining a sample x1The pathway of (a) further comprises: taking two samples u simultaneously from a set of unlabeled samplesjAnd ulTwo samples x1And x2Are mixed, um＝Mixλ(uj,ul)＝λuj+(1-λ)ul(ii) a Computing speculative tags
Figure BDA0002688004700000025
And
Figure BDA0002688004700000026
Figure BDA0002688004700000027
Figure BDA0002688004700000028
ujand ulRespectively representing two samples of the source unlabeled sample set, carrying out interpolation mixing on the labels,
Figure BDA0002688004700000031
will be provided with
Figure BDA0002688004700000032
As a sample x1And inputting the neural network. The sample mixing can increase the number of samples, fully utilize the existing sample data and reduce the sample data required by the training of the neural network model.
Preferably, in step C2), a preset number of samples are taken from the labeled sample set and input to the neural network, and then samples are taken from the labeled sample set or the unlabeled sample set for iteration. For steel surface unlabeled data we use training signal enhancement. For the unmarked data on the steel surface, the neural network model is low in identification precision at the beginning and is easy to misjudge the unmarked data on the steel surface, so that the unmarked sample on the steel surface can be fully utilized after the neural network model has certain precision and is judged, and the selected unmarked sample on the steel surface has purposiveness in adding training
Preferably, in step C2), when the accuracy of the neural network model reaches a set threshold, a sample is taken from the labeled sample or unlabeled sample set for iteration.
Preferably, in step C1), a threshold value is set
Figure BDA0002688004700000033
Figure BDA0002688004700000034
Figure BDA0002688004700000035
Wherein T is the current iteration number, T is the total iteration number, and k is the number of the steel surface defect types.
Preferably, in step C2), the loss function L is a cross-entropy loss function or a squared error loss function. The cross entropy loss function or the square error loss function can reflect the loss caused by the parameters of the neural network model during iteration.
The substantial effects of the invention are as follows: the neural network model can adapt to sample data without a label, so that the classifier can be trained by combining a labeled sample set and a non-labeled sample set to obtain the identification result of the steel surface defect, the labeled sample data required by the classifier is greatly reduced, the training cost of the classifier is reduced, and the application range of machine learning in the identification of the steel surface defect is expanded.
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FIG. 1 is a block diagram illustrating a method for identifying defects on a steel surface according to an embodiment.
Detailed Description
The following provides a more detailed description of the present invention, with reference to the accompanying drawings.
The first embodiment is as follows:
a steel surface defect identification method based on a semi-supervised deep learning algorithm is shown in figure 1 and comprises the following steps:
A) and acquiring an image of the surface of the steel and obtaining initial image sample data. Preprocessing the steel surface image: A1) carrying out normalization processing on the steel surface image; A2) and scaling the normalized steel surface image to enable the pixel width of the shorter side of the normalized steel surface image to be a preset value m, and then cutting the longer side of the normalized steel surface image to enable the steel surface image to be converted into a format of m multiplied by 3. The normalized steel surface image is easier to be processed by a classifier. Wherein m takes the value of 32x32, the pixel size of the steel surface defect can be usually shown by the pixel size of 32x32, and the meaningless data required to be processed by the classifier is reduced.
B) Dividing initial image sample data into a training data set and a verification data set according to a preset proportion, turning and shearing the training data set for enhancement, selecting 20% of the training data set for manual marking to serve as a labeled sample set, and taking the rest as a non-labeled sample set to add labels to the verification data set.
C) Inputting the training data set in the step B) into a neural network for iterative training, verifying by using samples in a verification sample set in every iteration N steps, and recording the accuracy of the model classifier until the accuracy reaches a preset threshold.
The method for iteratively training the neural network comprises the following steps: C1) setting a threshold ηt1、ηt2And ηt3Setting a threshold value
Figure BDA0002688004700000041
Figure BDA0002688004700000042
Wherein T is the current iteration number, T is the total iteration number, and k is the number of the types of the steel surface defects; C2) taking a sample x from a labeled sample set or an unlabeled sample set1Input to the neural network to obtain an output result f of the neural networkθ(x1)，fθ(x1) Including the probability of k defects, if sample x1Belongs to a set of labeled samples fθ(x1)|k≤ηt1Then sample x is sampled1Is the neural network f, is the loss function L, thetaθ(x) If the sample x is the training parameter of1Belongs to a set of unlabeled samples, fθ(x2)|k<ηt2And f isθ(x2)|k<ηt3Then sample x is sampled2The training result of (a) is incorporated into the loss function L; C3) calculating the gradient g from the loss function Lθ，
Figure BDA0002688004700000043
Updating a sliding average parameter theta', theta ═ theta +(1-alpha) theta, alpha is a sliding average rate, and then calculating the neural network fθ(x) Is equal to Step (theta', g)θ) The number of iterations t is added to 1, and the process is re-executed from step C1). Whether the loss function L is included is improved, so that the neural network model can adapt to sample data without labels.
D) Inputting the new steel surface image into the model classifier trained in the step C) to obtain a defect identification result of the steel surface image. The number of samples is increased by turning and shearing the training data set, and manual marking is performed by selecting 20%, so that the cost of sample marking is reduced.
In step C2), after a preset number of samples are taken from the labeled sample set and input to the neural network, samples are taken from the labeled sample set or the unlabeled sample set for iteration. For steel surface unlabeled data we use training signal enhancement. For the unmarked data on the steel surface, the neural network model is low in identification precision at the beginning and is easy to misjudge the unmarked data on the steel surface, so that the unmarked sample on the steel surface can be fully utilized after the neural network model has certain precision, and the selected unmarked sample on the steel surface has purposiveness in training.
The beneficial technical effects of this embodiment are: the neural network model can adapt to sample data without a label, so that the classifier can be trained by combining a labeled sample set and a non-labeled sample set to obtain the identification result of the steel surface defect, the labeled sample data required by the classifier is greatly reduced, the training cost of the classifier is reduced, and the application range of machine learning in the identification of the steel surface defect is expanded.
Example two:
in the embodiment of the method for identifying the defects of the steel surface based on the semi-supervised deep learning algorithm, in step C2), a sample x is obtained1The pathway of (a) further comprises: taking two samples x simultaneously from a set of labeled or unlabeled samplesjAnd xl(ii) a Two samples xjAnd xlMixing is carried out xm＝Mixλ(xj,xl)＝λxj+(1-λ)xlAnd the label is mixed by interpolation,
Figure BDA0002688004700000051
will be provided with
Figure BDA0002688004700000052
As a sample x1And inputting the neural network. Taking two samples u simultaneously from a set of unlabeled samplesjAnd ulTwo samples x1And x2Are mixed, um＝Mixλ(uj,ul)＝λuj+(1-λ)ul(ii) a Computing speculative tags
Figure BDA0002688004700000053
And
Figure BDA0002688004700000054
Figure BDA0002688004700000055
Figure BDA0002688004700000056
ujand ulRespectively representing two samples of the source unlabeled sample set, carrying out interpolation mixing on the labels,
Figure BDA0002688004700000057
will be provided with
Figure BDA0002688004700000058
As a sample x1Input neural netLinking the collaterals.
The neural network established in this embodiment includes: the first layer of network structure is a convolutional layer with a reception field size of 3 × 3, a convolutional kernel of 16, a step size of 1, and parameters of the convolutional layer of 3 × 3 × 3 × 16+16, namely 448, and the output is a feature map with 16 channels and a size of 32 × 32. The second and third tier network structures are residual structures with two 3x3 convolutional layers. The second layer of the network structure is a convolutional layer with a 3 × 3 convolutional kernel of 32 reception field size and a step size of 2, and outputs a feature map with 32 channels and a size of 16 × 16. The third layer of the network structure is a convolutional layer with a 3 × 3 convolutional kernel of 32 reception field size and a step size of 1, and outputs a feature map with 32 channels and a size of 16 × 16. The fourth and fifth layer network structures are residual structures with two 3 × 3 convolutional layers. The fourth layer of the network structure is a convolutional layer with a 3 × 3 convolutional kernel of 32 reception field size and a step size of 1, and outputs a feature map with 32 channels and a size of 16 × 16. The fifth layer network structure is a convolutional layer with a reception field size of 3 × 3 and a convolutional layer with a convolutional kernel of 32, the step size of 1, and a feature map with 32 channels and a size of 16 × 16 is output. The sixth and seventh layer network structures use a residual structure with two 3 × 3 convolutional layers. The sixth layer of the network structure is a convolutional layer with a 3 × 3 convolutional kernel of 64 reception field size and a step size of 2, and outputs a feature map with 64 channels and a size of 8 × 8. The seventh layer network structure is a convolutional layer with a reception field size of 3 × 3, a convolutional kernel of 64, a step size of 1, and a feature map with 64 channels and a size of 8 × 8 as output. The eighth and ninth tier network architectures employ a residual architecture with two 3x3 convolutional layers. The eighth layer of the network structure is a convolutional layer with a 3 × 3 convolutional kernel of 64 reception field size and a step size of 1, and outputs a feature map with 64 channels and a size of 8 × 8. The ninth layer of the network structure is a convolutional layer with a 3 × 3 convolutional kernel of 64 reception field size and a step size of 1, and outputs a feature map with 64 channels and a size of 8 × 8.
The tenth and eleventh network structures use a residual structure with two 3 × 3 convolutional layers. The tenth layer of the network structure is a convolutional layer with a reception field size of 3 × 3 and a convolutional layer with a convolutional kernel of 128, and the characteristic diagram with a step size of 2 is output and has a size of 4 × 4 and 128 channels. The eleventh layer of the network structure is a convolutional layer with a reception field size of 3 × 3 and a convolutional kernel of 128, and the convolutional layer with a step size of 1 outputs a feature map with 128 channels and a size of 4 × 4.
The twelfth and thirteenth layer architectures are residual architectures with two 3x3 convolutional layers. The twelfth layer of the network structure is a convolutional layer with a reception field size of 3 × 3 and a convolutional layer with a convolutional kernel of 128, and the characteristic diagram with a step size of 2 and an output of 128 channels and a size of 4 × 4. The thirteenth layer of the network structure is a convolutional layer with a 3 × 3 convolutional kernel of 128 reception field size and a step size of 1, and outputs a feature map with 128 channels and a size of 4 × 4.
Finally, the dropout and softmax classification layers, with a 3 × 3 receptive field size of maximum pooling with step size of 2 and random inactivation of 0.1, are used to calculate the probability that the output belongs to each class.
Compared with the embodiment, the method and the device can more fully utilize the existing sample data, and further reduce the number of the samples required by the neural network training. The rest steps are the same as the first embodiment.
The above embodiment is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the technical scope of the claims.

Claims (8)

1. A steel surface defect identification method based on a semi-supervised deep learning algorithm is characterized in that,
the method comprises the following steps:
A) acquiring an image of the surface of the steel and obtaining initial image sample data;
B) dividing initial image sample data into a training data set and a verification data set according to a preset proportion, turning and shearing the training data set for enhancement, selecting 20% of the training data set for manual marking to serve as a labeled sample set, and taking the rest as a non-labeled sample set to add labels to the verification data set;
C) inputting the training data set in the step B) into a model classifier for iterative training, verifying by using samples in a verification sample set in every iteration N steps, and recording the accuracy of the model classifier until the accuracy reaches a preset threshold;
D) inputting the new steel surface image into the model classifier trained in the step C) to obtain a defect identification result of the steel surface image.
2. The steel surface defect identification method based on semi-supervised deep learning algorithm as recited in claim 1,
in the step A), the steel surface image is preprocessed:
A1) carrying out normalization processing on the steel surface image;
A2) and scaling the normalized steel surface image to enable the pixel width of the shorter side of the normalized steel surface image to be a preset value m, and then cutting the longer side of the normalized steel surface image to enable the steel surface image to be converted into a format of m multiplied by 3.
3. The steel surface defect identification method based on semi-supervised deep learning algorithm as recited in claim 1,
in step C), the model classifier is a neural network, and the method for performing iterative training on the neural network includes: C1) setting a threshold ηt1、ηt2And ηt3；
C2) Taking a sample x from a labeled sample set or an unlabeled sample set1Input to the neural network to obtain an output result f of the neural networkθ(x1)，fθ(x1) Including the probability of k defects, if sample x1Belongs to a set of labeled samples fθ(x1)|k≤ηt1Then sample x is sampled1Is the neural network f, is the loss function L, thetaθ(x) The training parameters of (a) are set,
if sample x1Belongs to a set of unlabeled samples, fθ(x2)|k<ηt2And f isθ(x2)|k<ηt3Then sample x is sampled2The training result of (a) is incorporated into the loss function L;
C3) calculating the gradient g from the loss function Lθ，
Figure FDA0002688004690000011
Updating a sliding average parameter theta', theta ═ theta +(1-alpha) theta, alpha is a sliding average rate, and then calculating the neural network fθ(x) Is equal to Step (theta', g)θ) The number of iterations t is added to 1, and the process is re-executed from step C1).
4. The steel surface defect identification method based on semi-supervised deep learning algorithm as recited in claim 3,
in step C2), sample x is obtained1The pathway of (a) further comprises:
taking two samples x simultaneously from a set of labeled or unlabeled samplesjAnd xl；
Two samples xjAnd xlMixing is carried out xm＝Mixλ(xj,xl)＝λxj+(1-λ)xl，
The label is subjected to interpolation mixing, and then,
Figure FDA0002688004690000021
will be provided with
Figure FDA0002688004690000022
As a sample x1And inputting the neural network.
5. The steel surface defect identification method based on semi-supervised deep learning algorithm as recited in claim 3,
in step C2), sample x is obtained1The pathway of (a) further comprises:
taking two samples u simultaneously from a set of unlabeled samplesjAnd ulTwo samples x1And x2Are mixed, um＝Mixλ(uj,ul)＝λuj+(1-λ)ul；
Computing speculative tags
Figure FDA0002688004690000023
And
Figure FDA0002688004690000024
ujand ulRespectively representing two samples of the source unlabeled sample set, carrying out interpolation mixing on the labels,
Figure FDA0002688004690000025
Figure FDA0002688004690000026
will be provided with
Figure FDA0002688004690000027
As a sample x1And inputting the neural network.
6. The steel surface defect identification method based on semi-supervised deep learning algorithm as recited in claim 3,
in step C2), a preset number of samples are taken from the labeled sample set and input into the neural network, and then samples are taken from the labeled sample set or the unlabeled sample set for iteration.
7. The steel surface defect identification method based on semi-supervised deep learning algorithm as recited in claim 3,
in step C1), a threshold value is set
Figure FDA0002688004690000028
Wherein T is the current iteration number, T is the total iteration number, and k is the number of the steel surface defect types.
8. The steel surface defect identification method based on semi-supervised deep learning algorithm as recited in claim 3,
in step C2), the loss function L is a cross entropy loss function or a squared error loss function.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113947601A (en) * 2021-12-20 2022-01-18 武汉鑫国茂包装有限公司 Plastic product surface defect detection method and system based on semi-supervised learning

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180068429A1 (en) * 2015-04-15 2018-03-08 Institute Of Automation Chinese Academy Of Sciences Image Steganalysis Based on Deep Learning
CN108764281A (en) * 2018-04-18 2018-11-06 华南理工大学 A kind of image classification method learning across task depth network based on semi-supervised step certainly
CN111489326A (en) * 2020-01-13 2020-08-04 杭州电子科技大学 Copper foil substrate surface defect detection method based on semi-supervised deep learning

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180068429A1 (en) * 2015-04-15 2018-03-08 Institute Of Automation Chinese Academy Of Sciences Image Steganalysis Based on Deep Learning
CN108764281A (en) * 2018-04-18 2018-11-06 华南理工大学 A kind of image classification method learning across task depth network based on semi-supervised step certainly
CN111489326A (en) * 2020-01-13 2020-08-04 杭州电子科技大学 Copper foil substrate surface defect detection method based on semi-supervised deep learning

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113947601A (en) * 2021-12-20 2022-01-18 武汉鑫国茂包装有限公司 Plastic product surface defect detection method and system based on semi-supervised learning
CN113947601B (en) * 2021-12-20 2022-03-22 武汉鑫国茂包装有限公司 Plastic product surface defect detection method and system based on semi-supervised learning

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