CN111797930B - Fabric material near infrared spectrum identification and identification method based on twin network - Google Patents
Fabric material near infrared spectrum identification and identification method based on twin network Download PDFInfo
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Abstract
The invention discloses a twin network-based fabric material near infrared spectrum identification and identification method, which comprises the following steps: s100: collecting fabric samples made of different materials, and marking the types; s102: the acquisition of near infrared spectrum data of different types of samples is completed, and one or more near infrared spectrum data of each type of sample is ensured; preprocessing near infrared spectrum data to construct a near infrared spectrum sample database; s104: constructing a twin network, randomly extracting partial characteristics of spectrum sample data of the fabric material in a near infrared spectrum sample database as a near infrared spectrum sample subset, and training to obtain a twin network model; s106: acquiring spectral data of a fabric to be identified and identified, and randomly extracting a plurality of sample subsets; s108: and (4) inputting the plurality of sample subsets extracted randomly in the step (S106) into the twin network model trained in the step (S104), judging the allocation degree of the input sample subsets according to the output result, and judging the final prediction result of different subsets by voting.
Description
Technical Field
The invention belongs to the field of near infrared spectrum substance identification and identification, and particularly relates to a twin network-based fabric material near infrared spectrum identification and identification method.
Background
Currently, intelligent household appliances relate to various household appliance categories, and besides televisions, refrigerators, air conditioners and washing machines are increasingly integrated with intelligent attributes. But for many common consumers, the more intelligent the product, the more functions and the more tedious the operation. The intelligent washing machine also faces such a problem that the control panel becomes more and more complex. According to the traditional full-automatic washing machine, a certain program is preset on a computer control panel of the washing machine, different washing program options are provided for a user to select according to the material of clothes to be washed, when a certain program is selected during washing, the washing machine can automatically complete a series of operations such as soaking, washing, rinsing and dewatering, the washing is automatically stopped, and a buzzer sounds to inform airing. However, the general user does not have the ability to accurately distinguish the material of the clothes to be washed, may give out an inappropriate choice, and may directly select a standard mode more often, thereby reducing the washing effect. The patent publication No. CN201710047604.5, CN201910389465.3, material identification device, method, washing machine and control method thereof, and the like, automatically identify the material of the laundry, recommend the washing program, and optimize the washing mode to a certain extent. However, the clothes are made of various materials, and cotton, silk, terylene, hemp and the like have few samples and different contents, so that accurate identification and identification are difficult to achieve.
Based on the above current situation, how to provide a rapid identification and identification method for the situations of multiple fabric material types, unfixed types and few samples of each type is still a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the problems and provides a twin network-based fabric material near infrared spectrum identification and identification method, which can be used for constructing a rapid detection identification and identification method aiming at the special data format of near infrared spectrum, such as more fabric types, less samples and the like.
In order to achieve the purpose, the invention adopts the following technical scheme:
a near infrared spectrum identification and identification method for fabric materials based on twin networks comprises the following steps:
s100: collecting fabric samples made of different materials, and marking the types;
s102: the acquisition of near infrared spectrum data of different types of samples is completed, and one or more near infrared spectrum data of each type of sample is ensured; preprocessing near infrared spectrum data to construct a near infrared spectrum sample database;
s104: constructing a twin network, randomly extracting partial characteristics of spectrum sample data of the fabric material in a near infrared spectrum sample database as a near infrared spectrum sample subset, and training to obtain a twin network model;
s106: acquiring spectral data of a fabric to be identified and identified, and randomly extracting a plurality of sample subsets;
s108: and (4) inputting the plurality of sample subsets extracted randomly in the step (S106) into the twin network model trained in the step (S104), judging the allocation degree of the input sample subsets according to the output result, and judging the final prediction result of different subsets by voting.
Further, the step S100: collecting fabric samples of different materials, and marking categories, comprising: collecting fabric samples of different materials, collecting fabrics of various materials, classifying the materials with different content components independently, and collecting a small amount of samples in each category to form a fabric sample library; scanning samples in a sample library to obtain corresponding near infrared spectrum data Xi ═ { Xi1, Xi2.. xin }, and marking corresponding sample types Ynum, wherein i is the number of the samples, n is the dimensionality of the near infrared spectrum, and num is the number of the types of the samples.
Further, in S104, the constructing of the twin network includes a near infrared spectrum feature extraction subnet and a near infrared spectrum feature distance calculation decision subnet of the fabric material, the subnet is extracted by near infrared spectrum features, and near infrared spectrum data of a plurality of high latitudes are mapped to a low latitudes spectral feature vector; and (3) calculating the characteristic distance of the near infrared spectrum from the decision subnet, so that the characteristic spectrum distance of the same type of fabric material class is closer, otherwise, the distance of different types is farther, and judging the matching degree of the near infrared spectrum by the distance calculation decision subnet.
Further, the sub-network for extracting the near infrared spectrum characteristics of the fabric material comprises the following steps: the near infrared spectrum data is a one-dimensional sequence, two completely consistent one-dimensional convolution neural networks are adopted in the feature extraction subnet, the feature extraction subnet data is n-dimensional data of the near infrared spectrum, and m-dimensional spectrum feature vectors are output.
Further, the calculating of the near infrared spectrum characteristic distance of the fabric material decision subnet comprises: and the input of the near infrared spectrum characteristic distance calculation decision subnet is the output of m-dimensional spectrum characteristic vectors of the two one-dimensional convolutional neural networks, and the similarity of the two m-dimensional spectrum characteristic vectors is calculated and judged through the distance calculation decision subnet.
Compared with the prior art, the invention has the beneficial effects that:
the method aims at the condition that the near infrared spectrum identification and identification of the fabric material have more and unfixed targets and few samples of each type. Meanwhile, a subnet is extracted according to the characteristics of the specially designed twin network according to the condition of the one-dimensional sequence of the spectral data. The method is characterized in that a one-dimensional convolutional neural network with strong feature extraction capability is adopted to extract the near infrared spectrum data features of the fabric material, the twin network is used for comparing the similarity degree of random feature subsets of the near infrared spectrum data of the two fabric materials, and the output result is judged by voting. The method has the advantages of meeting the requirement of rapid identification of small samples and multi-class near infrared spectrum data. Meanwhile, the characteristic subset is adopted for comprehensive voting judgment, so that the model has higher generalization and is obviously creative in principle and implementation scheme.
Drawings
FIG. 1 is a twin network structure diagram of a fabric material near infrared spectrum identification and identification method in the fabric material near infrared spectrum identification and identification method based on the twin network of the present invention;
FIG. 2 is a schematic flow chart of a twin network-based fabric material near infrared spectrum identification and identification method of the invention;
FIG. 3 is a feature extraction subnet of a twin network based fabric material near infrared spectrum identification and authentication method of the present invention;
FIG. 4 is a complete network of a twin network based fabric material near infrared spectrum identification and characterization method of the present invention;
FIG. 5 shows the result of the near infrared spectrum identification and identification method of fabric material based on twin network training prediction.
Fig. 6 is two spectra of the same fabric at different locations.
Detailed Description
The present invention will be further described with reference to the following examples, which are intended to illustrate only some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, other embodiments used by those skilled in the art without any creative effort belong to the protection scope of the present invention.
Example 1:
as shown in figure 1, the twin network for identifying and identifying the near infrared spectrum of the fabric material mainly comprises two parts, namely a near infrared spectrum characteristic extraction subnet of the fabric material and a near infrared spectrum characteristic distance calculation decision subnet. The sub-network for extracting the near infrared spectrum characteristics of the fabric material consists of two completely consistent networks. According to the characteristics of the input near infrared spectrum data, the two networks are designed into one-dimensional convolution neural networks, and the function is to extract the characteristics of the near infrared spectrum data.
And the input of the near infrared spectrum characteristic distance calculation decision subnet is the output of two near infrared spectrum characteristic extraction subnets, and the similarity of two characteristic vectors is calculated and judged through the distance calculation decision subnet.
As shown in FIG. 2, the method for identifying and identifying the near infrared spectrum of the fabric material based on the twin network comprises the following steps:
step one, collecting fabric samples made of different materials, collecting fabrics made of various materials, classifying the materials with different content components independently, and collecting a small amount of samples in each category to form a fabric sample library.
And step two, scanning the samples in the sample library to obtain corresponding near infrared spectrum data Xi ═ Xi1, Xi2. Wherein i is the number of samples, n is the dimensionality of the near infrared spectrum, and num is the number of classes of the samples.
And step three, preprocessing the near infrared spectrum data, and constructing a fabric material training near infrared sample library.
Step four, building a twin network, and training a random feature subset pair of a near-infrared sample library by using the spectral data of the fabric material to obtain a fabric material near-infrared spectrum recognition twin network model;
and fifthly, scanning the near infrared spectrum data of the fabric sample to be detected for the classification problem, sequentially grouping the near infrared spectrum data with the near infrared spectrum data in the training sample library, randomly extracting a feature subset pair, inputting the feature subset pair into the twin network model, voting and judging the class with the highest similarity as the final class to be output, and completing the prediction of the class of the fabric material. For the identification problem, the near infrared spectrum data of the fabric sample to be identified is paired with the near infrared spectrum data of the class to be identified in the training sample library, the feature subset pairs are randomly extracted and input into the twin network model, and the similar fabric sample is judged to be identified by voting.
The method for constructing and training the spectrum data of the fabric material to obtain the twin network model specifically comprises the following steps:
the constructed twin network consists of two important parts, namely a near infrared spectrum characteristic extraction subnet and a near infrared spectrum characteristic distance calculation decision subnet of the fabric material. Through the feature extraction subnet, hundreds of thousands of dimensions of high latitude near infrared spectrum data are mapped into low latitude spectral feature vectors. And (3) calculating the characteristic distance of the near infrared spectrum from the decision subnet, so that the characteristic spectrum distance of the same type of fabric material class is closer, otherwise, the distance of different types is farther, and in short, the matching degree of the near infrared spectrum is judged by the distance calculation decision subnet.
The sub-network for extracting the near infrared spectrum characteristics of the fabric material is as follows:
because the near infrared spectrum data is a one-dimensional sequence, two completely consistent one-dimensional convolutional neural networks (1D CNN) are adopted by the feature extraction subnet, the feature extraction subnet data is n-dimensional data of the near infrared spectrum, and m is output as a spectrum feature vector.
The sub-network for calculating and deciding the near infrared spectrum characteristic distance is as follows:
the sub-network for calculating the near infrared spectrum characteristic distance of the fabric material comprises the following steps: and the input of the near infrared spectrum characteristic distance calculation decision subnet is the output of m-dimensional spectrum characteristic vectors of the two one-dimensional convolutional neural networks, and the similarity of the two m-dimensional spectrum characteristic vectors is calculated and judged through the distance calculation decision subnet.
The fabric material twin network training data is paired fabric material near infrared spectrum data. For the fabric material spectrum training sample data set of N samples, N (N-1) pairs of fabric material twin network inputs are input. Meanwhile, a feature subset pair which is randomly extracted into paired fabric material near infrared spectrum data is designed to serve as final training data, a small number of samples can provide sufficient training sets, when each pair of two groups of spectrum data come from the same class, the two groups of spectrum data are marked as the same class, otherwise, the two groups of spectrum data are different and serve as the output of a twin network.
After the twin network training is completed, the method can be used for identifying and identifying the fabric material sample to be tested. And for the identification problem, sequentially combining the fabric sample to be identified with the training sample library to form a sample set to be identified, sequentially inputting the sample set into a twin network model, wherein each pair of the sample set comprises a plurality of groups of random feature subsets, and the corresponding training sample set with the minimum voting output result and the highest matching degree is taken as an identification category. For the identification problem, only the sample to be identified and the class concentrated sample to be identified corresponding to the training sample set form a sample set to be tested, each pair has a plurality of groups of random feature subsets, and the size of the voting output result is the matching degree of the sample to be tested and the sample library.
As shown in fig. 3, a sub-network for extracting near infrared spectrum features of an embodiment is formed by combining a convolutional layer, a pooling layer, a full connection layer, and the like by using a one-dimensional convolutional neural network. The network structure can extract near infrared spectrum characteristics of different fabric materials.
As shown in fig. 4, a complete network is a specific embodiment of a twin network-based fabric material near infrared spectrum identification and identification method. And the output of the two near infrared spectrum characteristic extraction subnetworks is used as the input of a near infrared spectrum characteristic distance calculation decision subnet, and the near infrared spectrum characteristic distance calculation decision subnet adopts the Euclidean distance. The final output is the matching degree of the two input samples, and the smaller the distance, the higher the matching degree.
As shown in fig. 5, for the prediction result of the training in this embodiment, for the multi-class problem, the to-be-verified samples may be sequentially combined with the samples in the training sample set to form an input to-be-verified sample pair, and the corresponding training sample set with the minimum output is the class attribution of the to-be-verified samples. For the problem of fabric material identification, only the sample to be identified and the corresponding training sample set are required to form an input sample pair to be verified, and the matching degree of the fabric material is judged according to the output. From the result, the twin network model training convergence speed is high, the prediction can reach high accuracy, and the accuracy is obviously improved on the near infrared spectrum identification of the fabric material compared with the traditional machine learning method on the same sample set.
For example, fig. 6 shows two spectra of the same fabric at different positions, it can be seen that most positions of the two spectra are relatively similar, so that a higher accuracy can be obtained by using a pair of randomly extracted sub-features as training and prediction, and finally voting to determine a final result.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. A fabric material near infrared spectrum identification and identification method based on a twin network is characterized by comprising the following steps:
s100: collecting fabric samples made of different materials, and marking the types;
s102: the acquisition of near infrared spectrum data of different types of samples is completed, and one or more near infrared spectrum data of each type of sample is ensured; preprocessing near infrared spectrum data to construct a near infrared spectrum sample database;
s104: constructing a twin network, randomly extracting partial characteristics of spectrum sample data of the fabric material in a near infrared spectrum sample database as a near infrared spectrum sample subset, and training to obtain a twin network model;
s106: acquiring spectral data of a fabric to be identified and identified, and randomly extracting a plurality of sample subsets;
s108: and (4) inputting the plurality of sample subsets extracted randomly in the step (S106) into the twin network model trained in the step (S104), judging the allocation degree of the input sample subsets according to the output result, and judging the final prediction result of different subsets by voting.
2. The method for near infrared spectrum identification and identification of fabric materials based on twin networks as claimed in claim 1, wherein the step S100: collecting fabric samples of different materials, and marking categories, comprising: collecting fabric samples of different materials, collecting fabrics of various materials, classifying the materials with different content components independently, and collecting a small amount of samples in each category to form a fabric sample library; scanning samples in a sample library to obtain corresponding near infrared spectrum data Xi ═ { Xi1, Xi2.. xin }, and marking corresponding sample types Ynum, wherein i is the number of the samples, n is the dimensionality of the near infrared spectrum, and num is the number of the types of the samples.
3. The twin network-based fabric material near infrared spectrum identification and identification method as claimed in claim 1, wherein in S104, the construction of the twin network comprises a near infrared spectrum feature extraction subnet and a near infrared spectrum feature distance calculation decision subnet of the fabric material, the subnet is extracted by near infrared spectrum features, and near infrared spectrum data of a plurality of high latitudes are mapped to a low latitudes spectral feature vector; and (3) calculating the characteristic distance of the near infrared spectrum from the decision subnet, so that the characteristic spectrum distance of the same type of fabric material class is closer, otherwise, the distance of different types is farther, and judging the matching degree of the near infrared spectrum by the distance calculation decision subnet.
4. The twin network based fabric material near infrared spectrum identification and characterization method according to claim 3, wherein the fabric material near infrared spectrum feature extraction sub-network comprises: the near infrared spectrum data is a one-dimensional sequence, two completely consistent one-dimensional convolution neural networks are adopted in the feature extraction subnet, the feature extraction subnet data is n-dimensional data of the near infrared spectrum, and m-dimensional spectrum feature vectors are output.
5. The twin network based fabric material near infrared spectrum identification and characterization method according to claim 3, wherein the near infrared spectrum characteristic distance calculation decision subnet of the fabric material comprises: and the input of the near infrared spectrum characteristic distance calculation decision subnet is the output of m-dimensional spectrum characteristic vectors of the two one-dimensional convolutional neural networks, and the similarity of the two m-dimensional spectrum characteristic vectors is calculated and judged through the distance calculation decision subnet.
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| CN112801986A (en) * | 2021-02-01 | 2021-05-14 | 深圳市创景数据科技有限责任公司 | Textile material matching method, device, equipment, storage medium and system |
| CN113255838A (en) * | 2021-06-29 | 2021-08-13 | 成都数之联科技有限公司 | Image classification model training method, system and device, medium and classification method |
| CN114119984A (en) * | 2021-09-06 | 2022-03-01 | 池明旻 | Fiber component classification method based on one-dimensional convolutional neural network |
| CN114062305B (en) * | 2021-10-15 | 2024-01-26 | 中国科学院合肥物质科学研究院 | Single grain variety identification method and system based on near infrared spectrum and 1D-In-Resnet network |
| CN114062307A (en) * | 2021-10-25 | 2022-02-18 | 池明旻 | Data acquisition specification for near-infrared fabric fiber component nondestructive cleaning analysis |
| CN117171566B (en) * | 2022-08-17 | 2024-05-10 | 无锡迅杰光远科技有限公司 | Sample content identification method, device and storage medium |
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