CN110222792A - A kind of label defects detection algorithm based on twin network - Google Patents

A kind of label defects detection algorithm based on twin network Download PDF

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CN110222792A
CN110222792A CN201910538938.1A CN201910538938A CN110222792A CN 110222792 A CN110222792 A CN 110222792A CN 201910538938 A CN201910538938 A CN 201910538938A CN 110222792 A CN110222792 A CN 110222792A
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李竹
王韵涛
郭晨洁
盛庆华
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Hangzhou Dianzi University
Hangzhou Electronic Science and Technology University
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Abstract

The invention discloses a kind of label defects detection algorithm based on twin network, step S1: the acquisition of training set and test set；Step S2: network establishment and training；Step S3: test set verifying.Using technical solution of the present invention, build twin web tab defect detecting system, investment gets label data collection and is trained, classified again using softmax, it only needs to train the label of several types, even if tag types to be measured are not in training set when test, can equally carry out defects detection, preparation workload can be effectively reduced, detection efficiency is improved and reduces cost.

Description

A kind of label defects detection algorithm based on twin network
Technical field
The present invention relates to label manufacturing technology field, in particular to a kind of label defect detection based on twin network is calculated Method can be used for improving the management to label.
Background technique
As the carrier of product information, Commercial goods labels include bulk information, are played an important role in the management of product.But That Commercial goods labels can have a print defect, lacking number, leakage number is broken number, it is damaged the problems such as, this will generate huge shadow to the management of product It rings.Market circulation label is hundreds of millions of, and label quality is essential, and before label comes into the market, progress quality testing pole has must It wants.
Currently, the detection to label mostly uses artificial detection, hence it is evident that there are efficiency, the defects of accuracy rate is not high；Market is There is quality inspection instrument, too fat to move huge, expensive, maintenance difficulties are big, only support offline inspection；Popular OCR scheme is pursued comprehensively general Adaptive, efficiency and precision be not inevitably high.And at present using camera as image capture device, identify bar code two dimensional code and word Symbol, to realize that the scheme of the detection of dynamic labels be mainly template matching, background difference, frequency domain analysis, but template With that can not cope with rotation and scaling problem, and label to be detected need to be perfectly aligned with its correct label, to network requirement height；And it carries on the back Scape difference wants and trains label to match one by one in the sample of background template, and training sample is complex, with template matching one The correct tag template of sample, label to be detected need to be present in trained library；Method based on frequency domain analysis, there is when normal The problem of image is easy to produce erroneous detection when close with defect frequency.
Summary of the invention
The present invention is directed to the shortcomings that traditional image processing techniques, devises a kind of label defect inspection based on twin network Method of determining and calculating, to improve the training data problem of label detection.
Realize that technical scheme is as follows:
A kind of label defects detection algorithm based on twin network, comprising the following steps:
Step S1: obtain label data collection, wherein training set include various label defect types, and be divided into training set, Verifying collection and test set；
Step S2: building twin network and trains the twin network with training set data；
Step S3: test set verifies trained twin network, and simulates different type by using different type character Label；
Wherein, the step S1 further comprises:
Step S11: collecting label, intercepts character zone, obtains being used as simulating without being stained and being stained picture N Label；Modulus intends N in label1Zhang Zuowei verifying collection, N2Zhang Zuowei test set, N3Zhang Zuowei training set, wherein N1+N2+N3=N, And in three kinds of data sets, normal tag and M kind defective labels quantity obedience are uniformly distributed；
Step S12: the picture in training set is converted into tfrecord format, while for whether there is or not the pictures being stained to stick phase " label " is answered, in order to determine the y value of cost function calculation formula；Sequentially built batch is upset to training set later, so as to Realization is trained in batches；
The step S2 further comprises:
Step S21: twin network establishment, using 2 convolutional neural networks model VGG16 networks as convolutional neural networks Frame, wherein each VGG16 model includes 5 convolution sections, 13 convolutional layers, 5 maximization pond layers, 3 full articulamentums；Make Feature is extracted with 3*3 convolution kernel；
Input n to size be 224 × 224 image to two shared weights VGG16 model, export two 1000 Wei Te Levy f1、f2, pedestrian's identity of prediction input picture is respectively used to by identification label d, then compares high dimensional feature f with square layer1、 f2, it is made to predict verifying label s jointly；
Step S22: the training of twin network；Training set is put into twin network training, due to two VGG16 networks it Between share weight, two input by same convolutional neural networks study extraction feature；VGGNet is made of 5 sections of convolution, and every section Convolution includes 2~3 layers of convolutional layer, has a maximum pond layer to come compressed picture, dimensionality reduction de-redundant behind the last layer convolutional layer； Each section of convolutional layer convolution nucleus number all having the same, every to increase by one section, the convolution nucleus number of convolutional layer is increased by 1 in this section Times；It is a VGG16 network structure as follows:
First convolution section, the input picture size of the first layer convolution of VGG network is 224 × 224 × 3, by 64 The convolution kernel of 3*3*3 is 1 progress convolution with step-length, exports the characteristic pattern of a 224*224*64, second layer convolution input picture Size and output image size are 224*224*64.Then by with the maximization pond layer of 2*2, step-length 2 obtains 112* 112*64 characteristic pattern.
Second convolution section.This convolution section and first convolution section pixel, but after two convolutional layers of this section Output channel becomes 128, exports as 112*112*128.Pond layer is maximized as first segment maximization pond layer, therefore defeated Size becomes 56*56*128 out.
Third convolution section.The number of this section of convolutional layer becomes 3 by two before, and then output channel becomes 256, 56*56*256 is exported, by becoming 28*28*256 with consistent pond layer, Output Size before.
4th convolution section.The port number of convolutional layer is still double, becomes 512, and finally by pond layer, Output Size becomes At 14*14*512.
5th convolution section.The port number of this section of convolutional layer maintains 512, after maximizing pond layer, output Become 7*7*512.
After 13 layers of convolutional layer, VGG16 network enters 3 full articulamentums, and first and second full articulamentum has 4096 units, the full articulamentum of third have 1000 units, finally export the feature that dimension is 1000.
Step S23: by calculating loss function, pass through back-propagation algorithm training convolutional nerve net as reference Network optimizes the parameters such as corresponding weight biasing, convolutional neural networks is allowed preferably to extract feature, final to realize the complete of training sample U.S. fitting.The loss function (loss) of use is shown below:
Wherein, L is loss function value；
Y indicates input picture label similarity, and y=0 indicates that input sample input1 and input2 are dissimilar, y=1 then table Show that input sample is similar；
dnIndicate the Euclidean distance between two input samples, in which:
dn=| | Gw(X1)-Gw(X2)||
Margin is used in Gw(x) boundary is defined, is indicated when inputting a positive sample and a negative sample, only Can just have an impact to loss function when distance has less than this value.
The step S3 further comprises:
Step S31: one is known correct label in dual input；
Step S32: another channel inputs label to be measured；
Step S33: constructed fuction f (z, x) calculates similarity,
Wherein, z is to compare template image, and x is an equal amount of candidate image, and φ is convolution Embedding function, and b is indicated every Different bias on a position；
The output of the twin frame is a shot chart, if the two iamge descriptions is same object, returns to high score, Otherwise low point is returned；
Step S35: the classification accuracy rate by softmax real time monitoring network about training set and verifying collection, every completion Average accuracy of 100 output.Network every batch of trains 64 (32*2) pictures, and when iteration n times, loss stablizes intimate 0 and omits There is minor swing, the accuracy rate on training set stabilizes to 100%, and the classification accuracy on verifying collection is also stable more than 99%, Complete training.
In above-mentioned technical proposal, the specific implementation process is as follows:
(1) twin network establishment.The present invention is using 2 convolutional neural networks model VGG16 networks as convolutional Neural net Network frame, wherein each VGG16 model include 5 convolution sections, 13 convolutional layers, 5 maximization pond layers, 3 full articulamentums, Feature is extracted using 3*3 convolution kernel, is not uniquely both the convolution kernel number difference that each convolution section uses.Input n is to size 224 × 224 image exports two 1000 dimensional feature f to the VGG16 model of two shared weights1、f2, it is respectively used to predict defeated Enter pedestrian's identity of image by identification label d, then compares high dimensional feature f with square layer1、f2, it is made to predict verifying label jointly s。
(2) twin network is constituted.Since twin network is by 2 convolutional neural networks model VGG16 networks, two VGG16 Weight is shared between network, therefore can be considered as two inputs and be learnt to extract feature by same convolutional neural networks.VGGNet It is made of 5 sections of convolution, every section of convolution includes 2~3 layers of convolutional layer, there is a maximum pond layer behind the last layer convolutional layer Compressed picture, dimensionality reduction de-redundant.Each section of convolutional layer convolution nucleus number all having the same, every to increase by one section, convolutional layer in the section Convolution nucleus number be increased by 1 times.Starting convolution nucleus number is 64, and one convolution section of every process was with regard to double later, up to 512.
(3) network training.It is instructed being put into twin network to size for the training set of 224 × 224 image construction by n Practice, due to sharing weight between two VGG16 networks, two inputs can be considered as by same VGG16 network frame, learned It practises and extracts feature, carry out dimension specification, to calculate comparison loss function in feature space.To obtain the shorter training time It first loads VGG before the network of training label defect with the target of better training effect and is directed to MNIST handwritten numeral number According to the pre-training parameter of collection.
(4) by calculating loss function (loss), pass through back-propagation algorithm training convolutional nerve net as reference Network optimizes the parameters such as corresponding weight biasing, convolutional neural networks is allowed preferably to extract feature, final to realize the complete of training sample U.S. fitting.
(5) when carrying out test set verifying, different type label is simulated by using different type character, its in dual input One is known correct label, another channel inputs label to be measured, and tag types can be not present in training set, such as exist Training set is not present in experiment simulation simplified and unsimplified Hanzi and Japanese Latin etc. appear in test set.
(6) classify to Commercial goods labels.Since twinned structure solves target detection using the method for similarity-based learning Thus problem proposes function f (z, x), compares template image z and an equal amount of candidate image x, if the two iamge descriptions Be same object, return high score, otherwise return to low point.
(7) classification accuracy rate collected by softmax real time monitoring network about training set and verifying (distinguishes by two classification Bad label), every completion average accuracy of 100 output.Network every batch of trains 64 (32*2) pictures, when iteration n times, Loss stablizes intimate 0 slightly minor swing, the accuracy rate on training set and stabilizes to 100%, the classification accuracy on verifying collection Also stable more than 99%, complete training.
In above-mentioned technical proposal, characteristics of image is extracted by convolutional neural networks, and by reference to sample and to test sample Originally the distance metric in feature space, sorts out sample to be tested.
Compared with prior art, the present invention has the following technical effect that
(1) model proposed by the present invention has given full play to the characteristics of one shot learning may be implemented in twin network, Under the premise of not needing to expand a large amount of training data, customized it can increase the defect type for needing to detect and number of types Mesh.
(2) defect characteristic is extracted using neural network due to the present invention, mark label is treated, without spectrum signature etc. Limitation need to only have defect characteristic to the image of input network, or be normal tag.There is no specific requirement to picture material.Such as In label defects detection, when label to be detected is Chinese label, reference label can be for Japanese label, English label even The labels such as bar code.Therefore context of methods has higher selection freedom degree and wider application range.
(3) using the label defect detecting system of twin network establishment, most significant feature is image dual input, internal Structure includes two identical networks and shared weight.Subnet, which shares weight, means that training needs less parameter, also It is intended to less data and is not easy over-fitting, a large amount of training samples need not be prepared, so just can solve nerve net Common data deficiencies problem in network training, and be conducive to obtain desired result on test set.Also, compare template matching side Method can effectively reduce preparation workload, improve detection efficiency and reduce cost.
(4) twin network asks comparison loss as output, in other words can after the key feature for extracting two input pictures It extracts target similitude and contacts, be so easier to catch technicality.Especially tag class is ten hundreds of, if by each Kind needs the label detected to go into training, and workload is excessive.In twin network, it is only necessary to which the label of training several types is surveyed Even if tag types to be measured are not in training set when examination, defects detection can be equally carried out.
(5) compare twin network, traditional industrial label detection, often by background subtraction carry out template matching with And using Fourier transformation analysis of high frequency signal both, the former needs a large amount of templates, has raised cost, while such as light control System, dimension of picture unanimously waits requirements to also increase technology and cost burden, and the latter performs poor in label defects detection.And Label defects detection is carried out by building twin network, duplex input system is used as, traditional detection is compared, to equipment requirement It substantially reduces, does not also need the variables such as strict control light size, there is higher freedom degree, serious forgiveness and accuracy rate, pairing Case marker label are classified with four kinds of defective labels, are worked well, accuracy rate is more than 99%.
Detailed description of the invention
Fig. 1 is one embodiment of the invention training flow chart；
Fig. 2 is one embodiment of the invention VGG16 network structure；
Fig. 3 is the twin network detection structure figure of one embodiment of the invention；
Fig. 4 is the twin network structure of one embodiment of the invention；
Fig. 5 is one embodiment of the invention system testing structure chart；
Fig. 6 is one embodiment of the invention detection label schematic diagram.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with specific example, and referring to attached Figure, the invention will be further described.It should be appreciated that those skilled in the art, not departing from this Under the premise of inventive concept, various modifications and improvements can be made.These are all within the scope of protection of the present invention.
The present invention is based on the label defect detection algorithms of twin network, and training set to be put into net by establishing twin network Training in network is calculated loss function, similarity etc., and is separated the sheep from the goats label using two classification, and softmax real-time monitoring net is passed through Classification accuracy rate of the network about training set and verifying collection, average accuracy of 100 output, when the classification on verifying collection is quasi- True rate is also stable more than 99%, completes training.
As shown in Figure 1, the present invention includes 3 big steps, step S1: the acquisition of training set and test set；Step S2: network is taken It builds and trains；Step S3: test set verifying.It is as follows that here is that label defects detection detailed process is described in detail:
Step S1: the acquisition of training set and test set.As the data of the twin network of training, label defect kind in training set Class needs to enrich as far as possible.And since network is by training, it can be by scratch, burr ink dot goes out with filthy Edge Gradient Feature Come, therefore of less demanding to the type and content of known correct label, the test set tag types to be measured that can not sum are identical.
Step S11: collecting label, intercepts character zone, obtains being used as simulating without being stained and being stained picture N Label.Modulus intends N in label1Zhang Zuowei verifying collection, N2Zhang Zuowei test set, N3Zhang Zuowei training set, wherein N1+N2+N3=N, And in three kinds of data sets, normal tag and M kind defective labels quantity obedience are uniformly distributed.
Step S12: the picture in training set is converted into tfrecord format, while for whether there is or not the pictures being stained to stick phase " label " is answered, in order to determine the y value of cost function calculation formula.Sequentially built batch is upset to training set later, so as to Realization is trained in batches.
Step S2: network establishment and training.
Step S21: twin network establishment.The present invention is using 2 convolutional neural networks model VGG16 networks as convolution mind Through network frame, wherein each VGG16 model includes 5 convolution sections, 13 convolutional layers, 5 maximization pond layers, 3 connect entirely Layer is connect, is illustrated in fig. 2 shown below.
Network uses 3*3 convolution kernel to extract feature, and two 3 × 3 convolution kernels series connection are equivalent to one 5 on receptive field × 5 convolution kernel, but parameter is less, while CNN can also be reinforced to the learning ability of feature, it is unique different in each convolution It is that the convolution kernel number that each convolution section uses is different.
Be illustrated in fig. 3 shown below, input n to size be 224 × 224 image to two shared weights VGG16 model, output Two 1000 dimensional feature f1、f2, it is respectively used to predict that pedestrian's identity of input picture compares by identification label d, then with square layer High dimensional feature f1、f2, it is made to predict verifying label s jointly.
Step S22: the training of twin network.Training set is put into training in twin network, is illustrated in fig. 4 shown below, due to two Weight is shared between a VGG16 network, therefore can be considered as two inputs and be learnt to extract feature by same convolutional neural networks. VGGNet is made of 5 sections of convolution, and every section of convolution includes 2~3 layers of convolutional layer, there is a maximum pond behind the last layer convolutional layer Change layer and comes compressed picture, dimensionality reduction de-redundant.Each section of convolutional layer convolution nucleus number all having the same, it is every to increase by one section, in the section The convolution nucleus number of convolutional layer is increased by 1 times.It is a VGG16 network structure and input and output feature of image as follows:
First convolution section.The input picture size of the first layer convolution of VGG network is 224 × 224 × 3, by 64 The convolution kernel of 3*3*3 is 1 progress convolution with step-length, exports the characteristic pattern of a 224*224*64, second layer convolution input picture Size and output image size are 224*224*64.Then by with the maximization pond layer of 2*2, step-length 2 obtains 112* 112*64 characteristic pattern.
Second convolution section.This convolution section and first convolution section pixel, but after two convolutional layers of this section Output channel becomes 128, exports as 112*112*128.Pond layer is maximized as first segment maximization pond layer, therefore defeated Size becomes 56*56*128 out.
Third convolution section.The number of this section of convolutional layer becomes 3 by two before, and then output channel becomes 256, 56*56*256 is exported, by becoming 28*28*256 with consistent pond layer, Output Size before.
4th convolution section.The port number of convolutional layer is still double, becomes 512, and finally by pond layer, Output Size becomes At 14*14*512.
5th convolution section.The port number of this section of convolutional layer maintains 512, after maximizing pond layer, output Become 7*7*512.
After 13 layers of convolutional layer, VGG16 network enters 3 full articulamentums, and first and second full articulamentum has 4096 units, the full articulamentum of third have 1000 units, finally export the feature that dimension is 1000.
Step S23: by calculating loss function, pass through back-propagation algorithm training convolutional nerve net as reference Network optimizes the parameters such as corresponding weight biasing, convolutional neural networks is allowed preferably to extract feature, final to realize the complete of training sample U.S. fitting.The loss function (loss) of use is shown below:
Wherein, L is loss function value；
Y indicates input picture label similarity, and y=0 indicates that input sample input1 and input2 are dissimilar, y=1 then table Show that input sample is similar；
dnIndicate the Euclidean distance between two input samples, in which:
dn=| | Gw(X1)-Gw(X2)||
Margin is used in Gw(x) boundary is defined, is indicated when inputting a positive sample and a negative sample, only Can just have an impact to loss function when distance has less than this value.
Step S3: test set verifying.As shown in figure 5, simulating different type label by using different type character.
Step S31: one is known correct label in dual input, can be by scratch, burr ink since network is by training Point comes out with filthy Edge Gradient Feature, therefore of less demanding to the type and content of known correct label, also not sum Tag types to be measured are identical, can be number in experiment simulation, English character is also possible to Chinese character etc., as shown in Figure 6.
Step S32: another channel inputs label to be measured, does not limit content and type, even, tag types equally It can be not present in training set, the simplified and unsimplified Hanzi and Japanese Latin that such as training set is not present in experiment simulation Text etc. appears in test set.
Step S33: since twinned structure solves the problems, such as target detection using the method for similarity-based learning, letter is thus proposed Number f (z, x), compares template image z and an equal amount of candidate image x, if the two iamge descriptions is same object, returns High score is returned, otherwise returns to low point.
Network can be inputted for biggish search image by introducing full convolutional network, instead of time identical with template image size Select image.The twin network frame of full convolution can do primary assessment in the sub- window dense grid after all search full convolution of image, Calculate similarity.Similarity in order to obtain calculates f in conjunction with the cross-correlation layer of the characteristic pattern after convolution with convolution Embedding function φ (z, x):
The output of the twin frame is not a single similar score value but a shot chart, and b is indicated in each position The different bias set.
Step S35: classification accuracy rate (two classification by softmax real time monitoring network about training set and verifying collection Separate the sheep from the goats label), every completion average accuracy of 100 output.Network every batch of trains 64 (32*2) pictures, iteration N When secondary, loss stablizes intimate 0 slightly minor swing, the accuracy rate on training set and stabilizes to 100%, and the classification on verifying collection is quasi- True rate is also stable more than 99%, completes training.
To sum up for embodiment as it can be seen that the present invention can carry out defect detection to label, required data detect network compared to other It is few, solve the problems, such as data deficiencies common in neural network.Meanwhile when training, the data of several types, test need to be only trained When, even if tag types to be measured not in training set, can equally carry out defects detection.It is detected compared to traditional industrial label, Equipment requirement is greatly reduced, the variables such as strict control light size are not needed yet, there is a higher freedom degree, serious forgiveness and Accuracy rate classifies to qualified label and four kinds of defective labels, works well, accuracy rate is more than 99%.
Specific embodiments of the present invention are illustrated above, professional and technical personnel in the field is made to can be realized or use The present invention.Various modifications to these embodiments will be readily apparent to those skilled in the art, herein Defined General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.It needs It is to be understood that the present invention is not limited to the above specific embodiments, those skilled in the art can be in the model of claim Various deformations or amendments are made in enclosing, this is not affected the essence of the present invention.

Claims (1)

1. a kind of label defects detection algorithm based on twin network, which comprises the following steps:
Step S1: label data collection is obtained, wherein training set includes various label defect types, and is divided into training set, verifying Collection and test set；
Step S2: building twin network and trains the twin network with training set data；
Step S3: test set verifies trained twin network, and simulates different type label by using different type character；
Wherein, the step S1 further comprises:
Step S11: collecting label, intercepts character zone, obtains being used as simulation label without being stained and being stained picture N； Modulus intends N in label1Zhang Zuowei verifying collection, N2Zhang Zuowei test set, N3Zhang Zuowei training set, wherein N1+N2+N3=N, and three In kind data set, normal tag and M kind defective labels quantity obedience are uniformly distributed；
Step S12: the picture in training set is converted into tfrecord format, while for whether there is or not the pictures being stained to stick accordingly " label ", in order to determine the y value of cost function calculation formula；Sequentially built batch is upset to training set later, so as to reality Now train in batches；
The step S2 further comprises:
Step S21: twin network establishment, using 2 convolutional neural networks model VGG16 networks as convolutional neural networks frame Frame, wherein each VGG16 model includes 5 convolution sections, 13 convolutional layers, 5 maximization pond layers, 3 full articulamentums；It uses 3*3 convolution kernel extracts feature；
Input n to size be 224 × 224 image to two shared weights VGG16 model, export two 1000 dimensional feature f1、 f2, pedestrian's identity of prediction input picture is respectively used to by identification label d, then compares high dimensional feature f with square layer1、f2, make Its common prediction verifying label s；
Step S22: the training of twin network；Training set is put into training in twin network, due to total between two VGG16 networks Weight is enjoyed, two inputs learn to extract feature by same convolutional neural networks；VGGNet is made of 5 sections of convolution, every section of convolution Including 2~3 layers of convolutional layer, there is a maximum pond layer to come compressed picture, dimensionality reduction de-redundant behind the last layer convolutional layer；It is each The convolutional layer convolution nucleus number all having the same of section, every to increase by one section, the convolution nucleus number of convolutional layer is increased by 1 times in this section；Such as Under be a VGG16 network structure:
First convolution section, the input picture size of the first layer convolution of VGG network is 224 × 224 × 3, by 64 3*3*3 Convolution kernel, with step-length be 1 carry out convolution, export the characteristic pattern of a 224*224*64, second layer convolution input picture size It is 224*224*64 with output image size；Then by with the maximization pond layer of 2*2, step-length 2 obtains 112*112* 64 characteristic patterns；
Second convolution section, this convolution section and first convolution section pixel, but exported after two convolutional layers of this section Channel becomes 128, exports as 112*112*128；Pond layer is maximized as first segment maximization pond layer, therefore exports ruler It is very little to become 56*56*128；
The number of third convolution section, this section of convolutional layer becomes 3 by two before, and then output channel becomes 256, output 56*56*256, by becoming 28*28*256 with consistent pond layer, Output Size before；
4th convolution section, the port number of convolutional layer is still double, becomes 512, and finally by pond layer, Output Size becomes 14*14*512；
5th convolution section, the port number of this section of convolutional layer maintain 512, and after maximizing pond layer, output becomes 7*7*512；
After 13 layers of convolutional layer, VGG16 network enters 3 full articulamentums, and first and second full articulamentum has 4096 Unit, the full articulamentum of third have 1000 units, finally export the feature that dimension is 1000；
Step S23: excellent as reference by back-propagation algorithm training convolutional neural networks by calculating loss function Change the parameters such as corresponding weight biasing, convolutional neural networks is allowed preferably to extract feature, the final perfection for realizing training sample is quasi- It closes；The loss function (loss) of use is shown below:
Wherein, L is loss function value；
Y indicates input picture label similarity, and y=0 indicates that input sample input1 and input2 is dissimilar, and y=1 then indicates defeated It is similar to enter sample；
dnIndicate the Euclidean distance between two input samples, in which:
dn=| | Gw(X1)-Gw(X2)||
Margin is used in Gw(x) boundary is defined, is indicated when inputting a positive sample and a negative sample, only distance Having when being less than this value can just have an impact to loss function；
The step S3 further comprises:
Step S31: one is known correct label in dual input；
Step S32: another channel inputs label to be measured；
Step S33: constructed fuction f (z, x) calculates similarity,
Wherein, z is to compare template image, and x is an equal amount of candidate image, and φ is convolution Embedding function, and b is indicated in each position The different bias set；
The output of the twin frame is a shot chart, if the two iamge descriptions is same object, returns to high score, otherwise Return to low point；
Step S35: the classification accuracy rate by softmax real time monitoring network about training set and verifying collection, it is every to complete 100 times Export an average accuracy；Network every batch of trains 64 (32*2) pictures, and when iteration n times, loss stablizes intimate 0 slightly small echo Dynamic, the accuracy rate on training set stabilizes to 100%, and the classification accuracy on verifying collection is also stable more than 99%, completes instruction Practice.
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