CN116958739A - Attention mechanism-based carbon fiber channel real-time dynamic numbering method - Google Patents
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Abstract
A carbon fiber channel real-time dynamic numbering method based on an attention mechanism relates to the technical field of image processing. Obtaining sampling data by using an industrial camera; preprocessing the sampling data, wherein the preprocessing comprises training a channel prediction network and marking carbon fiber channel numbers in actual scenes; and outputting the processing result to obtain the marking number of the carbon fiber yarn channel. The invention adopts a visual transducer network to process carbon fiber images acquired by a linear array industrial camera, and sequentially carries out real-time dynamic numbering on carbon fiber yarns from top to bottom on image data. And under the condition of no obvious distinguishing characteristics, better processing is carried out on the data such as doubling or broken yarn and the like.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a carbon fiber yarn channel real-time dynamic numbering method based on an attention mechanism.
Background
In the production process of the carbon fiber yarn, the problem of surface defects (broken yarn, joint and the like) is prominent, and the quality of carbon fiber yarn products (such as manufacturing carbon fiber composite materials, reinforced carbon fiber concrete structures and the like) is seriously threatened. The surface defects are generated because of the fact that the carbon fiber defects are difficult to locate and trace due to the fact that certain viscosity exists on the surface of the yarn with low solidification speed or the yarn is mixed due to uneven impregnation of an oiling agent in the production of the carbon fiber, and the quality and the service life of a carbon fiber product are seriously influenced by the defects.
Because of the complex and diversified production environments, the quality level of the carbon fiber yarn images acquired by the industrial linear camera is uneven, and the characteristic of distinguishing yarn paths is not obvious. In addition, the carbon fiber yarn in the actual environment has various changes, and is easy to break, shift in the position of the carbon yarn and the like. The traditional image processing algorithm cannot cope with the complex data, so that the accuracy of the screw channel prediction cannot be guaranteed.
Due to the problems of doubling, broken filaments and the like, the algorithm based on the traditional image processing on the market is not applicable at present. That is, there are a plurality of carbon filaments in the image data that overlap together or that appear intermediate discontinuous, and these carbon filaments are collected by an industrial camera and often have no distinguishing property in the visual sense, nor have obvious distinguishing features in the image. Conventional algorithms have difficulty processing such data. Therefore, how to number the doubling or breakage and other conditions more accurately without obvious distinguishing characteristics is a worth discussing problem.
Disclosure of Invention
The invention provides a real-time carbon fiber yarn channel numbering algorithm based on an attention mechanism, which utilizes a transducer to process carbon fiber images acquired by a linear array industrial camera, outputs the prediction results of carbon fiber yarns and backgrounds in the images, and realizes the determination and numbering of carbon fiber yarn channels.
A carbon fiber channel real-time dynamic numbering method based on an attention mechanism comprises the following steps:
step S1: obtaining sampling data by using an industrial camera;
step S2: preprocessing the sampled data;
step S3: and outputting the processing result to obtain the marking number of the carbon fiber yarn channel.
Preferably, the preprocessing of the sampled data in step S2 of the present invention includes the step S21 of training the silk channel prediction network, which specifically includes the following steps:
step S211: preprocessing data; specific:
will sample the dataDivide into training sets->And test set->Two parts; wherein x is i Original image of carbon fiber yarn of 3 Xh Xw, y i A 1 xw silk label, a silk position value of 1, and a background position value of 0;
for the training set, each original image of the carbon fiber yarn is respectively subjected to the preprocessing operations of Resize, normalization, random enhancement of color brightness and saturation, and random vertical overturning, and each yarn path label is required to be adjusted according to the width of the original image of the carbon fiber yarn after Resize; the method comprises the steps that a bilinear interpolation algorithm is selected for the Resize operation of an original image of the carbon fiber yarn, and a bicubic interpolation algorithm is selected for the adjustment of a label;
for the verification set, only the pretreatment operation of Resize and normalization is needed for each original image of the carbon fiber yarn, and each yarn path label is needed to be adjusted according to the width of the image after Resize;
the sizes of images after the training set and the verification set are the same;
step S212: outputting a classification predicted value and a position predicted value;
step S213: calculating loss;
step S214: optimizing and predicting network parameters;
step S215: and outputting the optimal prediction model.
Preferably, the output classification predicted value and the position predicted value of step S212 of the present invention; the specific process is as follows:
initializing parameters, including Linear mapping layer Linear, transducer coding network, classification network and wire numbering network; an optimizer Adam; marking the carbon wire data set;
taking an image of [3, i, w ] as an input sample, starting from the i-th position, i.e. taking images from [3, i, w ] to [3, f+h, w ];
then using a size of [ h, w ] p ]Grid pair [3, h, w ]]Dividing the input sample of (2) to obtainAn image block; compressing the first 3 dimensions to get +.>Is input to the computer; let 3 Xh Xw p ＝d，/>Obtaining an input sample of the i-th layer->
Linear mapping of input samples:splice-on position embedding vectorInput to get a transducer network ≡>
Will x i Inputting the code into a transducer coding network to obtain a code output o i ＝Transformer(x i )∈R (d+1)×k ；
O is set to i Respectively send into the classification network f cls And a lane numbering network f loc Classification prediction to obtain k input blocksAnd position prediction +.>
Preferably, the calculation loss of step S213 of the present invention; the specific process is as follows:
calculating classification prediction lossesWhere MSELoss is the mean square error loss, y cls ∈R 1×k The value of k real categories is 0 or 1; when the area occupied by the carbon wire in the corresponding kth input image is larger than a certain threshold value (0.5), the value is 1, otherwise, the value is 0;
calculating position prediction losses wherein yloc ∈R 1×k Is a real label corresponding to the channel number;
wherein ,j=1, …, N is the number of all tracks for the true value corresponding to the kth image block;
calculate total loss = l cls +l loc Updating network parameters using Adam optimizer, including Linear mapping layer Linear, transducer coding network, classification network f cls Silk road numbering network f loc 。
Preferably, the output optimal prediction model of step S215 of the present invention comprises the following specific processes:
optimizing the whole network parameters by using an Adam optimizer;
adding 1 to the value of i, and repeating the whole process from the step 1; until i＝w-w p The i value is classified as 0, the next picture is replaced, and the whole process is repeated from the step 1;
calculating loss under the verification set by using the verification set, selecting a model with minimum loss as an optimal prediction network, and outputting a final silk channel prediction network
Preferably, the preprocessing of the sampled data in the step S2 of the present invention further includes labeling the carbon fiber channel number in the actual scene, and the specific process is as follows:
for test dataCarrying out the operations of restoration and normalization on each original picture;
inputting the processed test image into the final screw channel prediction network obtained in step S215Obtain the output predictive vector +.>The carbon fiber channel numbers are numbered.
The method comprises the steps of training a visual transducer by utilizing images and labeling data of carbon fiber filaments in the early stage, and predicting the channel numbers in the carbon fiber filament images. The model has the advantages of less parameter, quick and efficient calculation, less operation needed by post-treatment, realization of real-time dynamic numbering of the filament tracks of the carbon fiber filaments, and certain generalization capability and stability.
Drawings
FIG. 1 is a flow chart of the real-time dynamic numbering method of the present invention.
FIG. 2 is a flow chart of the training phase of the present invention;
FIG. 3 is a flow chart of the prediction phase of the present invention;
fig. 4 is a network configuration diagram of the present invention.
Detailed Description
The technical scheme of the invention is described in detail below with reference to the accompanying drawings:
as shown in fig. 1, a method for dynamically numbering carbon fiber channels in real time based on an attention mechanism comprises the following steps:
step S1: obtaining sampling data by using an industrial camera;
step S2: preprocessing the sampled data;
step S3: and outputting the processing result to obtain the marking number of the carbon fiber yarn channel.
As shown in fig. 2, the preprocessing of the sampled data in step S2 of the present invention includes step S21 of training a silk channel prediction network, which specifically includes the following steps:
step S211: preprocessing data; specific:
will sample the dataDivide into training sets->And verification set->Two parts; wherein x is i Original image of carbon fiber yarn of 3 Xh Xw, y i A carbon fiber yarn path label of 1 Xw, a carbon fiber yarn path position value of 1, a background position value of 0, N train 、N val The total number of samples of the training set and the verification set respectively;
for the training set, preprocessing operations of Resize, normalization, color disturbance and random vertical overturning are respectively carried out on each carbon fiber yarn original image, and each yarn channel label is required to be adjusted according to the width of the carbon fiber yarn original image after Resize; the method comprises the steps that a bilinear interpolation algorithm is selected for the Resize operation of an original image of the carbon fiber yarn, and a bicubic interpolation algorithm is selected for the adjustment of a label;
the specific operation of normalization is as follows:mu is the mean of the sample and sigma is the variance.
The specific operation of the color disturbance is as follows: for one pixel (r, g, b), it is assumed that the value of each channel is between [0,1 ]. ColorJitter will do the following for each channel:
wherein Ci For the value of the current channel,for the new value after processing, α is the adjustment intensity, typically [0,1]The fraction in between, rand (-1, 1) is a fraction uniformly distributed in [ -1,1]Random numbers in between. After the above operation, the new color is wherein ：
where trunc () is a rounding function, rounds the calculation to an integer, and converts it to a pixel value on the image.
Random vertical flip: current image sample x according to a certain probability p i Vertical flipping is performed randomly, with p set to 0.5.
Resize: let us assume that we want to compute the size m×n of the image Resize as m×n (M < M, N < N), first, the corresponding position of each pixel after scaling down in the original image, and let us say that the position of the (x, y) th pixel in the original image after scaling down is (i, j), then: x=i×m/M, y=j×n/N. The bilinear interpolation algorithm and the bicubic interpolation algorithm in the Resize are respectively:
(1) Bilinear interpolation algorithm:
1. calculating coordinates of four nearest neighbor pixels of each position (i, j) of the image after Resize at the corresponding position (x, y) in the original image: (x) 1 ，y 1 )，(x 1 ，y 2 )，(x 2 ，y 1) and (x2 ，y 2), wherein x1 and y1 To the maximum satisfy x 1 I is less than or equal to i and y 1 Integer less than or equal to j, x 2 and y2 To minimum satisfy x 2 Not less than i and y 2 Integer of j.
2. For each coordinate position (i, j) in the Resize image, the pixel value is f (i, j), which is calculated as follows:
f(i，j)＝(1-w)(1-h)f(x 1 ，y 1 )+w(1-h)f(x 2 ，y 1 )+(1-w)hf(x 1 ，y 2 )+whf(x 2 ，y 2 )，
wherein w= (i-x) 1 )/(x 2 -x 1 ) And h= (j-y) 1 )/(y 2 -y 1 ) Is a weight coefficient.
(2) Bicubic interpolation algorithm:
we need to calculate the pixel value f (i, j) of the scaled down image from the value of (x, y). Specifically, for the (i, j) th pixel, its pixel value can be calculated by the following formula:
wherein The position of the upper left corner pixel of the 16 pixels adjacent to (i, j) is indicated. g (k, l) represents the pixel values of the kth row and the first column, w k-a，l-b Is a weighting coefficient calculated from (x, y)To (a) and (b). Specifically, the weighting coefficient can be calculated from the following formula:
w k-a，i-b ＝w(s)·w(t)
where s=k-x+1, t=l-y+1, w(s) and w (t) represent weighting coefficients in s, t directions, respectively, which can be calculated by the following formula:
the w (z) coefficient in the above formula is called a bicubic interpolation weight function, and its function is to control the smoothness of interpolation, when z is 0, the value of w (z) is the largest, the gray value of the corresponding pixel has the largest influence on the interpolation result, and when z is far away from 0, the value of w (z) gradually decreases, and the influence on the interpolation result is smaller and smaller.
For verification set dataThe original image of each carbon fiber wire only needs to do the preprocessing operation of Resize (bilinear interpolation algorithm, the same as above) and normalization, and each wire label needs to adjust Resize (bicubic interpolation algorithm, the same as above) according to the width of the image Resize;
the sizes of images after the training set and the verification set are the same;
step S212: outputting a classification predicted value and a position predicted value; the specific process is as follows:
initializing parameters, including Linear mapping layer Linear, transducer coding network, classification network and wire numbering network; an optimizer Adam; an original image of carbon fiber yarn and a yarn path label corresponding to the original image; as shown in figure 4 of the drawings,
taking an image of [3, i, w ] as an input sample, starting from the i-th position, i.e. taking images from [3, i, w ] to [3, i+h, w ];
then using a size of [ h, w ] p ]Grid pair [3, h, w ]]Dividing the input sample of (2) to obtainAn image block; compressing the first 3 dimensions to get +.>Is input to the computer; let 3 Xh Xw p ＝d，/>Obtain the input sample of the i-th position +.>k is the total number of the image blocks divided, and d is the dimension of each image block after compression. R real set vector space, R { d x k }, represents vector space of d by k dimensions; if the width w of each image block p Setting to 5, marking a picture to obtain +.>The number of training samples can realize that the labeling quantity of the total picture is controlled within 100.
Linear mapping of input samples:splice-on position embedding vectorInput to get a transducer network ≡>
Will x i Inputting the code into a transducer coding network to obtain a code output o i ＝Transformer(x i )∈R (d+1)×k ；
Output o of code i Respectively send into the classification network f cls And a lane numbering network f loc Classification prediction to obtain k input blocksAnd position prediction +.>Specifically calculated as +.> wherein Wcls and bcls Respectively a classification network f cls σ (x) is a ReLU activation function, and the calculation formula is σ (x) =max (0, x); wherein Wloc and bloc Respectively numbering network f for silk channels loc Weight parameters and bias of (a).
Step S213: calculating loss; the specific process is as follows:
calculating classification prediction lossesWhere MSELoss is the mean square error loss, y cls ∈R 1×k The value of the real category corresponding to the k image blocks is 0 or 1; when the occupied area of the carbon fiber channels in the corresponding kth input image block is larger than a certain threshold value (0.5), the value is 1, otherwise, the value is 0;
calculating position prediction losses wherein yloc ∈R 1×k Is a real label corresponding to the channel number;
wherein ,j=1, …, N are all the number of tracks, j is the index of the track from left to right; calculate total loss = l cls +l loc Updating network parameters using Adam optimizer, including Linear mapping layer Linear, transducer coding network, classification network f cls Silk road numbering network f loc . The purpose of the classification network is to assist learning, and only the lane numbering network is used for lane prediction in the test stage.
Step S214: optimizing and predicting network parameters;
step S215: the optimal prediction model is output, and the specific process is as follows:
optimizing the whole network parameters by using an Adam optimizer;
adding 1 to the value of i, and repeating the whole process from the step 1; until i=w-w p The i value is classified as 0, the next picture is replaced, and the whole process is repeated from the step 1;
calculating loss under the verification set by using the verification set, selecting a model with minimum loss as an optimal prediction network, and outputting a final silk channel prediction network
As shown in fig. 3, the preprocessing of the sampled data in step S2 of the present invention further includes labeling of carbon fiber channel numbers in actual scenes, and the specific process is as follows:
for test dataCarrying out the operations of restoration and normalization on each original picture;
inputting the processed test image into the final screw channel prediction network obtained in step S215Obtain the output predictive vector +.>Specifically calculated as +.>From left to right, when one or more consecutive values greater thanWhen the value of the threshold value (0.5) is reached, the current position or the continuous region is judged as a new carbon fiber yarn, and the number of the carbon fiber yarn is added with 1 (the initial value is 0). The carbon fiber channel numbers are numbered accordingly.
The invention utilizes a linear array industrial camera, and utilizes images and labeling data of carbon fiber filaments in the early stage to train a visual transducer to predict the number of the channel in the carbon fiber filament images. The model has the advantages of less parameter, quick and efficient calculation, less operation needed by post-treatment, realization of real-time dynamic numbering of the filament tracks of the carbon fiber filaments, and certain generalization capability and stability. The invention can directly number the silk channels of the image by only a shallow visual transducer structure, has less overall model parameters, less hardware cost and high calculation efficiency, and can number the carbon fiber silk channels in real time.
Claims (6)
1. A carbon fiber channel real-time dynamic numbering method based on an attention mechanism is characterized by comprising the following steps:
step S1: obtaining sampling data by using an industrial camera;
step S2: preprocessing the sampled data;
step S3: and outputting the processing result to obtain the marking number of the carbon fiber yarn channel.
2. The attention mechanism-based carbon fiber channel real-time dynamic numbering method according to claim 1, wherein the preprocessing of the sampled data in step S2 comprises the training of a channel prediction network in step S21, and the specific process is as follows:
step S211: preprocessing data; specific:
will sample the dataDivide into training sets->And test set->Two parts; wherein x is i Original image of carbon fiber yarn of 3 Xh Xw, y i A 1 xw silk label, a silk position value of 1, and a background position value of 0;
for the training set, each original image of the carbon fiber yarn is respectively subjected to the preprocessing operations of Resize, normalization, random enhancement of color brightness and saturation, and random vertical overturning, and each yarn path label is required to be adjusted according to the width of the original image of the carbon fiber yarn after Resize; the method comprises the steps that a bilinear interpolation algorithm is selected for the Resize operation of an original image of the carbon fiber yarn, and a bicubic interpolation algorithm is selected for the adjustment of a label;
for the verification set, only the pretreatment operation of Resize and normalization is needed for each original image of the carbon fiber yarn, and each yarn path label is needed to be adjusted according to the width of the image after Resize;
the sizes of images after the training set and the verification set are the same;
step S212: outputting a classification predicted value and a position predicted value;
step S213: calculating loss;
step S214: optimizing and predicting network parameters;
step S215: and outputting the optimal prediction model.
3. The attention mechanism-based carbon fiber channel real-time dynamic numbering method according to claim 2, wherein the output classification predicted value and the position predicted value of step S212; the specific process is as follows:
initializing parameters, including Linear mapping layer Linear, transducer coding network, classification network and wire numbering network; an optimizer Adam; marking the carbon wire data set;
taking an image of [3, i, w ] as an input sample, starting from the i-th position, i.e. taking images from [3, i, w ] to [3, i+h, w ];
then using a size of [ h, w ] p ]Grid pair [3, h, w ]]Dividing the input sample of (2) to obtainAn image block; compressing the first 3 dimensions to get +.>Is input to the computer; let 3 Xh Xw p ＝d，/>Obtaining an input sample of the i-th layer->
Linear mapping of input samples: splicing position embedded vector->Input to get a transducer network ≡>
Will x i Inputting the code into a transducer coding network to obtain a code output o i ＝Transformer(x i )∈R (d+1)×k ；
O is set to i Respectively send into the classification network f cls And a lane numbering network f loc Classification prediction to obtain k input blocksAnd position prediction +.>
4. The attention mechanism based carbon fiber channel real-time dynamic numbering method according to claim 3, wherein the calculation loss of step S213; the specific process is as follows:
calculating classification prediction lossesWhere MSELoss is the mean square error loss, y cls ∈R 1×k The value of k real categories is 0 or 1; when the area occupied by the carbon wire in the corresponding kth input image is larger than a certain threshold value (0.5), the value is 1, otherwise, the value is 0;
calculating position prediction losses wherein yloc ∈R 1×k Is a real label corresponding to the channel number;
wherein ,j=1, …, N is the number of all tracks for the true value corresponding to the kth image block;
calculate total loss = l cls +l loc Updating network parameters using Adam optimizer, including Linear mapping layer Linear, transducer coding network, classification network f cls Silk road numbering network f loc 。
5. The attention mechanism-based carbon fiber channel real-time dynamic numbering method according to claim 4, wherein the outputting of the optimal prediction model in step S215 comprises the following specific steps:
optimizing the whole network parameters by using an Adam optimizer;
i value is added with 1, and the whole process is repeated from the 1 st stepA program; until i=w-w p The i value is classified as 0, the next picture is replaced, and the whole process is repeated from the step 1;
calculating loss under the verification set by using the verification set, selecting a model with minimum loss as an optimal prediction network, and outputting a final silk channel prediction network
6. The convolutional neural network-based carbon fiber channel identification and numbering method according to claim 5, wherein the preprocessing of the sampled data in the step S2 further comprises labeling of carbon fiber channel numbers in actual scenes, and the specific process is as follows:
for test dataCarrying out the operations of restoration and normalization on each original picture;
inputting the processed test image into the final silk channel prediction network obtained in step s215Obtain the output predictive vector +.>The carbon fiber channel numbers are numbered.
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