CN110288013A - A Defect Label Recognition Method Based on Block Segmentation and Multiple Input Siamese Convolutional Neural Networks - Google Patents

Abstract

The defective labels recognition methods based on block segmentation and the multiple twin convolutional neural networks of input that the invention discloses a kind of, step S1: carries out block dividing processing for label picture；Step S2: the multiple twin convolutional neural networks of input are trained using block block label picture data set, obtain the trained multiple twin residual error network model of input；Step S3: defective labels are identified and is classified using trained model.Using technical solution of the present invention, block block label data collection is trained, determine classification belonging to defect, correctly classified in conjunction with adaboost algorithm, the calculation amount and complexity of defective labels identification are thus greatly reduced, while also effectively increasing the accuracy of defective labels identification classification.

Description

A Defect Marker Based on Block Segmentation and Multiple Input Siamese Convolutional Neural Networks Sign identification method

technical field

The invention relates to the field of defect label detection and identification in the field of detection and identification in industrial production activities, in particular to a defect label identification method based on block segmentation and multiple input twin convolutional neural networks.

Background technique

With the development of society, nowadays, many commodities on the market have labels. Labels have the function of marking the key information of products. They play an increasingly important role in people's work and life. At the same time, the quality problems of labels are also increasing. more and more people's attention. However, during the production process of labels, due to factors such as production technology and mechanical precision, the produced labels often have many quality problems, such as label damage, poor printing, including overprinting, underprinting, missing and scratched characters. mark phenomenon, etc.; therefore, the label defect detection link is very important. At the same time, due to the wide variety of defect labels, the detection and classification of defect labels becomes very difficult. At present, there are three main methods for defect label detection and identification:

1. In industrial production, workers on the production line check the quality of labels through human eye comparison, keep labels with qualified quality, and discard unqualified labels.

The existing problem is that there are various drawbacks in the method of manual detection of label quality, such as slow detection speed, low precision, high cost, and long-term manual detection can easily cause human fatigue.

2. The defect label detection method based on differential processing, prepare the reference label, let the label to be tested perform differential processing on the reference label, and can detect the label image.

The existing problems are: inappropriate reference labels prepared, false detections caused by different label contents, and false detections caused by uneven illumination.

3. The defect label detection method based on frequency domain processing can detect the defect label by using the feature of the defect signal with high frequency.

The existing problems are: the label image and the area with similar frequency of defect information cause false detection, and there are requirements for label content, etc.

Therefore, there is an urgent need to develop a set of defect label detection methods for industrial production lines. The method can automatically detect the label printing content, accurately identify the position and category of defective labels, and can also customize and expand the types of defective labels, and the system is extremely flexible.

SUMMARY OF THE INVENTION

In view of this, it is indeed necessary to provide a defect label recognition method based on block segmentation and multiple input Siamese convolutional neural network. The block label data set is trained to determine the category of the defect and the location of the defect, and then combined with adaboost The algorithm performs correct classification, thereby greatly reducing the calculation amount and complexity of defect label identification, and also effectively improving the accuracy of defect label identification and classification. On this basis, we can customize the label images of defect types for training, and the system has good flexibility and scalability.

In order to overcome the defects of the prior art, the technical scheme of the present invention is as follows:

A defect label recognition method based on block segmentation and multiple input Siamese convolutional neural network, including the following steps:

Step S1: perform block segmentation processing on the label image;

Step S2: Use the block label image data set to train the multi-input Siamese convolutional neural network to obtain a trained multi-input Siamese residual network model;

Step S3: Identify and classify defect labels using the trained network model.

Wherein, the S1 further includes:

Step S11: Obtain the label image data set, and perform block cutting processing;

Step S12: store the cut label picture block in the block label picture database;

The step S11 further includes:

Step S111: the width of the label in the label picture set is w, and the height is h;

Step S112: block cutting the label image with block blocks whose width and height are n;

Step S113: Each label image is divided into w/n*h/n blocks.

The step S2 further includes:

Step S21: obtain block label picture data set from block label picture database;

Step S22: use the block label image data set to train the multi-input siamese residual network model;

The step S22 further includes:

Step S221: Initialize the weight distribution of the training set, each training sample is given the same weight at the beginning: 1/N, which can be expressed as follows:

D ₁ =(w ₁₁ , w ₁₂ ... w _1i ..., w _1N ),

In the above formula, D1 represents the data set with the specified weight in the first round of iteration. In the subscript W, the first number indicates the number of iterations, the second number is the index of the sample, and the number of samples is N;

Step S222: Suppose we want to perform M rounds of iterations, that is, select M optimal weak classifiers, and then start the iteration, where m represents the number of iterations.

for(int m=1; m <= M; m++);

Step S223: Use the training data set with the weight distribution D _m to learn to obtain an optimal weak classifier:

G _m (x): χ→, {-1, +1}

where G _m (x) represents the classifier learned from the training set with the weight distribution D _m in the mth round, and the classification result is χ→,{-1,+1}

Step S224: Select a weak classifier G with the lowest current error rate as the m-th basic classifier Gm, and calculate the weak classifier: G _m (x): χ→, {-1, +1}, calculate Gm (x ) classification error rate on the training dataset:

where em represents the error rate, _xi represents the input sample, _yi represents the classification result, and w _mi represents the weight of the ith sample in the _mth iteration.

Step S225: Calculate Denote the weight of Gm(x) in the final classifier:

Step S226: Update the weight distribution of the training data set and perform the m+1th round of iteration:

D _m+1 = (w _m+1 , 1 , w _{m+1, 2} ... w _{m+1, i} ..., w _{m+1, N} ),

where D _m+1 represents the data set in the m+1th iteration, W _m+1 represents the weight of the data set in the m+1th iteration, Z _m represents the normalization constant, Represents the weight of Gm(x) in the final classifier, and Gm is the weak classifier with the lowest error rate in the mth iteration.

After the weights are updated, the weights of the misclassified samples by the basic classifier Gm(x) increase, while the weights of the correctly classified samples decrease;

Step S227: After the above M rounds of iteration, the strong classifier can be combined as the final model of the trained model:

The sign function is a sign function. It returns 1 if it is greater than 0, -1 if it is less than 0, and 0 if it is equal to 0. Gm(x) is the classifier obtained in the mth iteration, is the weight of the current classifier in the final classifier.

Compared with the prior art, the present invention has the beneficial effects:

Efficiency: 1. The present invention performs block segmentation processing on the label image, which can efficiently capture the defect features of the label image when the defect information is weak, and can quickly identify the defect location. 2. The present invention utilizes the block label picture database to store label picture information, and adopts the multi-input twin convolutional neural network composed of the residual network of deep learning to train the label picture data set, and obtains an efficient multi-input twin residual network model. , which improves the classification performance of defect label images, improves the defect that the high complexity of the existing defect label images easily leads to false detection, and improves the efficiency of identification.

Accuracy: 1. The present invention trains the block label data set, and establishes an accurate multi-input twin residual network model. Since the twin network has multiple input terminals to share parameters, the same type of features can be extracted and mapped to features The vector has a high similarity, and finally the defect label image is classified into the most similar reference label category, which improves the fact that the existing label image is easy to extract useless features, causing classification errors, and improves the accuracy of classification. sex. 2. Combined with the adaboost algorithm of the improved label image, further training the wrongly classified label image, further improving the accuracy of the category prediction of the defect label image, and improving the defect label classification accuracy of the existing defect label detection and identification technology. .

Scalability: The present invention uses a multi-input twin convolutional neural network to train the data. One input of the twin network is used as the label to be tested, and the rest of the inputs are reference labels. Classification is performed by similarity, which not only improves the classification efficiency. Accuracy, and the types of defect labels can be customized, which adapts to the shortcomings of a wide variety of defect labels and difficult identification.

Description of drawings

Fig. 1 is a kind of flowchart of the defect label identification method based on block segmentation and multiple input twin convolutional neural network provided by the present invention;

2 is a detailed flowchart of a method for identifying defect labels based on block segmentation and multiple-input twin convolutional neural networks provided by the present invention;

3 is a schematic diagram of step S1 of a defect label identification method based on block segmentation and multiple input twin convolutional neural networks provided by the present invention;

4 is a structural diagram of a multiple input twin network model of a defect label identification method based on block segmentation and multiple input twin convolutional neural networks provided by the present invention;

5 is a structural diagram of a residual network of a defect label identification method based on block segmentation and multiple input twin convolutional neural networks provided by the present invention;

6 is a detailed flowchart of step S22 of a method for identifying defect labels based on block segmentation and multiple-input twin convolutional neural networks provided by the present invention;

The following specific embodiments will further illustrate the present invention in conjunction with the above drawings.

Detailed ways

The technical solutions provided by the present invention will be further described below with reference to the accompanying drawings.

When the label image and defect information are relatively large, it is not easy to capture useful information when extracting features. Based on this, we block the label image; For the classification and detection effect of hierarchical defect features, we use a residual network to extract the defect features; in order to ensure that the same type and the most similar features are captured and improve the accuracy of classification, we use a twin residual network to Capture defect features; at the same time, in order to improve the accuracy of classification, we use a multi-input siamese network for similarity comparison, and also use the adaboost algorithm to repeatedly train the misclassified label images. The invention provides a defect label identification method based on block segmentation and multiple input twin convolutional neural network.

The present invention provides a method for identifying defect labels based on block segmentation and multiple-input twin convolutional neural networks. Figures 1 and 2 show the defect label identification system based on block segmentation and multiple-input twin convolutional neural networks of the present invention. In other words, the present invention includes three major steps, step S1: performing block segmentation on the label image; step S2: using the block label image data set to train the multiple input siamese residual network model; step S3: using the trained multiple input siamese residual The network classifies and identifies defect label images;

Referring to Fig. 3, step S1 is based on the block label picture database, and the label picture with width w and height h is divided into w/n*h/n small label block blocks;

Referring to FIG. 4 , the twin convolutional neural model used in the present invention is composed of multiple residual networks, each residual network corresponds to one input, one input terminal is the label information to be measured, and the other input terminals are reference label information. .

Referring to FIG. 5 , a residual module in ResNet-34 applied in the present invention is formed by two layers of convolution and an identity map, which effectively alleviates the influence of gradient disappearance, so that the number of layers of the network model can be greatly increased.

Referring to Figure 6, step S22 uses the adaboost algorithm to train the multiple twin residual network model, which specifically includes the following steps:

Step S221: Initialize the weight distribution of the training set, each training sample is given the same weight at the beginning: 1/N, which can be expressed as follows:

D _{1 =} (w ₁₁ , w ₁₂ ... w _1i ..., w _1N ),

In the above formula, D1 represents the data set with the specified weight in the first round of iteration. In the subscript W, the first number indicates the number of iterations, the second number is the index of the sample, and the number of samples is N;

Step S222: Suppose we want to perform M rounds of iterations, that is, select M optimal weak classifiers, and then start the iteration, where m represents the number of iterations.

for(int m=1; m <= M; m++);

Step S223: Use the training data set with the weight distribution D _m to learn to obtain an optimal weak classifier:

G _m (x): χ→, {-1, +1}

where G _m (x) represents the classifier learned from the training set with the weight distribution D _m in the mth round, and the classification result is χ→,{-1,+1}

Step S224: Select a weak classifier G with the lowest current error rate as the m-th basic classifier Gm, and calculate the weak classifier: G _m (x): χ→, {-1, +1}, calculate Gm (x ) classification error rate on the training dataset:

where em represents the error rate, _xi represents the input sample, _yi represents the classification result, and w _mi represents the weight of the ith sample in the _mth iteration.

Step S225: Calculate Denote the weight of Gm(x) in the final classifier:

Step S226: Update the weight distribution of the training data set and perform the m+1th round of iteration:

D _m+1 = (w _m+1 , 1 , w _{m+1, 2} ... w _{m+1, i} ..., w _{m+1, N} ),

where D _m+1 represents the data set in the m+1th iteration, W _m+1 represents the weight of the data set in the m+1th iteration, Z _m represents the normalization constant, Represents the weight of Gm(x) in the final classifier, and Gm is the weak classifier with the lowest error rate in the mth iteration.

After the weights are updated, the weights of the misclassified samples by the basic classifier Gm(x) increase, while the weights of the correctly classified samples decrease;

Step S227: After the above M rounds of iteration, the strong classifier can be combined as the final model of the trained model:

The sign function is a sign function. It returns 1 if it is greater than 0, -1 if it is less than 0, and 0 if it is equal to 0. Gm(x) is the classifier obtained in the mth iteration, is the weight of the current classifier in the final classifier.

The descriptions of the above embodiments are only used to help understand the method and the core idea of the present invention. It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can also be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

The above description of the disclosed embodiments enables any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (1)

1. a kind of defective labels recognition methods based on block segmentation and the multiple twin convolutional neural networks of input, feature exist In, comprising the following steps:

Step S1: label picture is subjected to block dividing processing；

Step S2: the multiple twin convolutional neural networks of input are trained using block block label picture data set, are obtained more Twin residual error network model is inputted again；

Step S3: defective labels are identified and is classified using trained multiple input twin residual error network model；

Wherein, the S1 further comprises:

Step S11: label picture database is obtained, and block cutting process is carried out to label picture；

Step S12: the label picture block block after cutting is stored in block block label picture database；

The step S11 further comprises:

Step S111: the tag width in label picture database is w, is highly h；

Step S112: block cutting is carried out to label picture with width and height are the block block of n；

S113: every label picture of step is divided into w/n*h/n block block；

The step S2 further comprises:

Step S21: block block label picture data set is obtained from block block label picture database；

Step S22: the multiple twin residual error network model of input of block block label picture data set training is used；

The step S22 further comprises:

Step S221: initializing the weight distribution of training set, each training sample is endowed identical weight when most starting: 1/N can be expressed as follows:

D1 indicates the data set with specified weight of first round iteration in above formula, and in W subscript, it is that first digit, which is shown, A few wheel iteration, second digit are the index of sample, and the quantity of sample is N；

Step S222: assuming that we will carry out M wheel iteration, that is, selecting M optimal Weak Classifiers, next start iteration, Wherein what m was indicated is the number of iteration；

For (int m=1；M <=M；m++)；

Step S223: D is distributed using with weight_mTraining dataset study, obtain an optimal Weak Classifier:

G_m(x): χ →, { -1 ,+1 }

Wherein G_m(x) what is indicated is to be distributed D to weight in m wheel_mThe classifier that learns of training set, the knot of classification Fruit be χ →, { -1 ,+1 }；

Step S224: the minimum Weak Classifier G of an error current rate is chosen as m-th of basic classification device Gm, and is calculated weak Classifier: G_m(x): χ →, { -1 ,+1 } calculates the error in classification rate of Gm (x) on training dataset:

Wherein e_mIndicate error rate, x_iThat indicate is input sample, y_iThat indicate is classification results, w_miWhat is indicated is changed in m wheel The weight of i-th of sample in generation；

Step S225: it calculatesIndicate the weight of Gm (x) in final classification device:

Step S226: the weight distribution for updating training dataset carries out m+1 wheel iteration:

D_m+1=(w_M+1,1,w_m+1,2...w_m+1,i...,w_m+1,N),

Wherein D_m+1Indicate the data set in the m+1 times iteration, W_m+1That indicate is the weight of data set in the m+1 times iteration, Z_mTable What is shown is normaliztion constant, What is indicated is weight of the Gm (x) in final classification device, and Gm is m Take turns the Weak Classifier that error current rate is minimum in iteration；

After right value update, just to be increased by the weight of basic classification device Gm (x) misclassification sample, and by correct classification samples Weight reduces；

Step S227: after top M takes turns iteration, can combine strong classifier, as the final type of trained mould:

Sign function is sign function, is greater than 0 and returns to 1, -1 is returned less than 0, is equal to 0 and returns to 0；Gm (x) is in m wheel iteration Obtained classifier,It is current class device weight shared in final classifier.