CN113743525B - Fabric material identification system and method based on luminosity three-dimensional - Google Patents
Fabric material identification system and method based on luminosity three-dimensional Download PDFInfo
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- CN113743525B CN113743525B CN202111074359.XA CN202111074359A CN113743525B CN 113743525 B CN113743525 B CN 113743525B CN 202111074359 A CN202111074359 A CN 202111074359A CN 113743525 B CN113743525 B CN 113743525B
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- 239000004744 fabric Substances 0.000 title claims abstract description 82
- 239000000463 material Substances 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000012545 processing Methods 0.000 claims abstract description 25
- 238000002310 reflectometry Methods 0.000 claims abstract description 24
- 238000000605 extraction Methods 0.000 claims abstract description 18
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 7
- 239000000284 extract Substances 0.000 claims abstract description 3
- 238000012706 support-vector machine Methods 0.000 claims description 34
- 238000012549 training Methods 0.000 claims description 17
- 239000013598 vector Substances 0.000 claims description 14
- 238000007789 sealing Methods 0.000 claims description 9
- 238000005516 engineering process Methods 0.000 claims description 3
- 238000002372 labelling Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 description 3
- 229920000742 Cotton Polymers 0.000 description 1
- 239000004677 Nylon Substances 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 229920001778 nylon Polymers 0.000 description 1
- 229920000728 polyester Polymers 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 210000002268 wool Anatomy 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention discloses a fabric material identification system and method based on luminosity three-dimensional. The system comprises an acquisition module, an image processing module, a feature extraction module and an identification module. Wherein the collecting device is provided with light sources and cameras in different directions in the opaque sealed box. The camera collects images of different material fabrics under different light sources through a shooting window at the bottom end. The image processing module calculates a reflectivity map and a normal map of the image through a photometric stereo method according to the image and the corresponding light information, inputs the reflectivity map and the normal map into the characteristic extraction module, extracts characteristics through a convolutional neural network, and finally classifies the materials of the fabric by using the identification module. The fabric material identification method based on luminosity three-dimensional comprises image acquisition and processing, feature extraction and classification identification.
Description
Technical Field
The invention belongs to the technical field of data processing, relates to an artificial intelligence deep learning data processing method, and particularly relates to a fabric material identification system and method based on luminosity three-dimensional.
Background
The fabric material identification and classification technology has wide application scenes in production and life, including the fields of robot design and industrial detection. The technology can provide a means for analyzing the material properties of the fabric, provides assistance for the identification and classification of the material properties of the fabric, and improves the decision-making efficiency.
Because fabric pictures can be affected by a variety of factors such as shape, reflection characteristics, illumination, and viewing angle, identifying fabric materials from the pictures is a challenging task. The existing method for identifying the material of the fabric needs to use multiple cameras to acquire multi-angle images, so that equipment is complex, the use process is complex, the images acquired by different cameras also relate to the corresponding problem, and the quick identification of multiple fabrics is difficult to realize. Meanwhile, as the material of the fabric is not only represented in the color, but also more represented in the concave-convex shape of the surface, the microscopic geometry of the surface of the fabric also contains information useful for identifying and classifying the fabric, and the current identification method lacks in acquiring and using the information.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a fabric material identification system and method based on luminosity three-dimensional, which uses an acquisition module to shoot fabric images under a plurality of different light sources, uses an identification module to analyze microscopic geometric shape information of the surface of the fabric, and identifies the fabric material.
A fabric material identification system based on luminosity three-dimensional comprises an acquisition module, an image processing module, a feature extraction module and an identification module.
The acquisition module comprises an opaque sealing box, a camera and a luminous array. The bottom of the sealing box is provided with a shooting opening, and the camera and the luminous array are fixed inside the sealing box. The shooting direction of the camera is aligned with the shooting port. The light emitting array is used for providing light rays in different directions. The acquisition module is tightly covered on the surface of the fabric to be identified, a plurality of pictures under different angles of light rays are shot for one fabric, and the directions of the light rays are marked and then transmitted to the image processing module.
Preferably, the light emitting array is fixed between a camera head and a shooting port of the camera and comprises a plurality of light emitting diodes with different orientations.
Preferably, the acquisition module further comprises a microprocessor fixed inside the sealed box, and the microprocessor is used for controlling the luminous array and the camera and wirelessly transmitting the obtained picture and the corresponding light direction to the image processing module.
The image processing module calculates the reflectivity and the normal direction of each pixel point in the image according to a plurality of images of the same fabric and corresponding light direction information by a photometric stereo method, and the reflectivity map and the normal map of the three-dimensional shape are obtained after the normal directions are summarized.
The feature extraction module inputs the reflectivity map and the normal map obtained by the image processing module into a convolutional neural network, and extracts feature vectors capable of reflecting the three-dimensional microstructure of the surface of the fabric. The identification module identifies the feature vector obtained by the feature extraction module, and material classification of the fabric is completed.
Preferably, the convolutional neural network is a VGG-M model initialized by using the disclosed pre-training parameters, the input of the VGG-M model is a reflectivity map and a normal map, and the output is a feature vector.
Preferably, the identification module comprises K support vector machines, and the material classification of the fabric is realized by using a one-to-many support vector machine method. Wherein K is the material type of the fabric to be identified.
A fabric material identification method based on luminosity three-dimensional comprises the following steps:
step one, image acquisition
A large number of fabric images are collected, and a plurality of images of the same fabric at the same angle and in different light directions are taken as one sample. Labeling a label of a sample according to the material of the fabric to be used as a training set.
Preferably, at least 3 images of the same fabric in different light directions are acquired.
Step two, image processing
And calculating the reflectivity and the normal direction of each pixel point in the image according to the light direction information of the sample in the training set by using a light stereoscopic method, and collecting the normal direction to obtain a reflectivity map and a normal map of the three-dimensional shape of the sample.
Step three, feature extraction
And (3) inputting the reflectivity map and the normal map obtained in the step two into a VGG-M model, and outputting the feature vector of the corresponding sample by the VGG-M model.
Step four, classification training
And aiming at samples of K different labels, using K support vector machines to carry out classification training. Defining 1 positive class of the support vector machine as a label, wherein the rest labels are negative classes of the support vector machine, and the positive class labels of the K support vector machines are not repeated. And (3) respectively inputting the feature vectors obtained in the step (III) into K support vector machines, and taking the class corresponding to the support vector machine with the largest classification value as a classification result of the sample.
Step five, identifying the fabric
And (3) acquiring a fabric image which is not labeled and to be identified, processing according to the second step, inputting the fabric image into a VGG-M model for feature extraction, and inputting the fabric image into K support vector machines trained in the fourth step to obtain labels corresponding to the fabric, thereby completing identification.
The invention has the following beneficial effects:
the acquisition module in the method can acquire multiple light source images of the fabric to be identified simply and rapidly by matching the sealed box with the light source with the monocular camera for shooting, and provides an original image for identifying the fabric material for the subsequent processing module. Compared with the existing similar devices, the module has the advantages of low cost, small size, portability and easy use, and the switching of the device between the fabrics to be identified can be realized by simply lifting and moving the device; meanwhile, the sealing box is made of opaque materials, is not influenced by an external light source in use, has low requirements on the use environment, and avoids a complex device structure caused by complex picture input requirements.
Drawings
FIG. 1 is a schematic diagram of an acquisition module in an embodiment;
fig. 2 is a schematic view of a photographing port of the acquisition module in the embodiment;
FIG. 3 is a flow chart of a method for identifying fabric materials.
Detailed Description
The invention is further explained below with reference to the drawings;
a fabric material identification system based on luminosity three-dimensional comprises an acquisition module, an image processing module, a feature extraction module and an identification module.
As shown in fig. 1, the acquisition module comprises a sealed box, a camera 2, a microprocessor circuit board 3 and a light emitting array 5. The sealed box consists of a square casing 1 with a closed top end and light-proof, and a matrix base 4. The square-shaped housing 1 is fixed on a rectangular base 4. The camera 2, the microprocessor 3 and the light emitting array 5 are fixed inside the square-shaped housing 1. As shown in fig. 2, a photographing port is provided at the center of the rectangular base 4. The shooting direction of the camera 2 is opposite to the shooting window in the center of the rectangular base 4. The light emitting array 5 is located between the camera 2 and the photographing window. The light emitting array 5 comprises four sets of leds oriented differently. The microprocessor circuit board 3 controls the light emitting diode to switch on and off states, so that light rays in different directions can be provided for the acquisition module. The fabric is tiled on a desktop, the rectangular base 4 is stably placed on the surface of the fabric, the microprocessor circuit board 3 controls the camera 3 to shoot pictures of the same fabric when different light emitting diodes are lightened, and after the directions of light rays are marked, the pictures are transmitted to calculation through the universal bus serial interface for subsequent image processing and recognition.
Photometric stereo is a method in the field of computer vision that uses different pictures from three or more light sources at the same viewing angle to estimate the reflectivity and normal direction of each pixel point within an image. The image processing module calculates the reflectivity and the normal direction of the fabric according to the image and the light information acquired by the acquisition module through a photometric stereo method, and the reflectivity map and the normal map of the three-dimensional shape are obtained after the normal direction is summarized, wherein the reflectivity map and the normal map can reflect the microscopic aggregate shape of the surface of the fabric.
The characteristic extraction module inputs the reflectivity map and the normal map obtained by the image processing module into a convolutional neural network, and analyzes the three-dimensional microstructure of the fabric. The convolutional neural network is a VGG-M model and comprises 5 convolutional layers and 3 full-connection layers, and the initialization parameters inside the model use the pre-training parameters of the VGG-M. The model output may represent a feature vector of the three-dimensional microstructure.
The identification module uses a one-to-many support vector machine method to realize material classification of the fabric, and comprises K support vector machines, wherein K is the material type of the fabric to be identified. The one-to-many support vector machine method trains K support vector machines when classifying K categories, and for each support vector machine, the positive example only has one category, and the rest K-1 categories are all negative categories. The positive classes of the K support vector machines are in one-to-one correspondence with the K classes, when the support vector machines are used for prediction, samples to be predicted are respectively input into the K support vector machines, and the class corresponding to the support vector machine with the largest classification value is used as a classification result of the samples. The identification module identifies the feature vector obtained by the feature extraction module, and material classification of the fabric is completed.
As shown in fig. 3, the fabric material identification method based on luminosity three-dimensional comprises the following steps:
step one, image acquisition
And acquiring images of fabrics made of different materials, and taking 4 images of the same fabric at the same angle and in different light directions as one sample. Labeling the sample label according to the material on the fabric component label comprises 5 kinds of cotton cloth, nylon, wool, silk and polyester fiber. The acquired images were separated into training and test sets at a 3:1 ratio using a random sampling method without replacement.
Step two, image processing
And (3) calculating the reflectivity and the normal direction of each pixel point in the image according to the light direction information of the sample in the training set by using a light stereoscopic method, and obtaining a reflectivity map and a normal map of the three-dimensional shape of the sample after the normal direction is summarized.
Step three, feature extraction
And (3) inputting the reflectivity map and the normal map obtained in the step two into a VGG-M model, and outputting the feature vector of the corresponding sample by the VGG-M model.
Step four, classification training
For samples of 5 different labels in the training set, 5 support vector machines are used for classification training. Defining 1 positive class of the support vector machine as one label, wherein the rest 4 labels are negative classes of the support vector machine, and the positive class labels of the 5 support vector machines are not repeated. And (3) respectively inputting the feature vectors obtained in the step (III) into 5 support vector machines, and taking the class corresponding to the support vector machine with the largest classification value as a classification result of the sample.
Step five, identifying the fabric
And (3) inputting the samples in the test set into the VGG-M model for feature extraction after processing according to the step (II), and inputting the samples into K support vector machines trained in the step (IV) to obtain labels corresponding to the fabric, and testing the classification performance of the support vector machines.
Claims (6)
1. A fabric material identification system based on luminosity is three-dimensional, which is characterized in that: the device comprises an acquisition module, an image processing module, a feature extraction module and an identification module;
the acquisition module comprises an opaque sealing box, a camera and a light emitting array; the bottom of the sealing box is provided with a shooting port, and the camera and the luminous array are fixed in the sealing box; the shooting direction of the camera is aligned with the shooting port; the light emitting array is used for providing light rays in different directions; the bottom of the sealing box is tightly and stably placed on the surface of the fabric to be identified, a plurality of pictures under different angles of light rays are shot for one fabric, and the directions of the light rays are marked and then transmitted to the image processing module;
the image processing module calculates the reflectivity and the normal direction of each pixel point in the image according to a plurality of images of the same fabric and corresponding light direction information by a photometric stereo method, and the normal direction is summarized to obtain a reflectivity map and a normal map of a three-dimensional shape;
the characteristic extraction module inputs the reflectivity map and the normal map obtained by the image processing module into a convolutional neural network, and extracts characteristic vectors capable of reflecting the three-dimensional microstructure of the surface of the fabric; the identification module identifies the feature vector obtained by the feature extraction module to finish material classification of the fabric;
the acquisition module further comprises a microprocessor fixed inside the sealing box, wherein the microprocessor controls the camera to shoot pictures when the luminous array is lightened, and the obtained pictures and the corresponding light directions are transmitted to the image processing module.
2. The fabric material identification system based on luminosity three-dimensional system of claim 1, wherein: the light emitting array is fixed between a camera of the camera and the shooting port and comprises a plurality of light emitting diodes with different orientations.
3. The fabric material identification system based on luminosity three-dimensional system of claim 1, wherein: the convolutional neural network is a VGG-M model initialized by using the disclosed pre-training parameters; the VGG-M model comprises 5 convolution layers and 3 full connection layers, wherein the input is a reflectivity map and a normal map, and the output is a feature vector.
4. The fabric material identification system based on luminosity three-dimensional system of claim 1, wherein: the identification module performs training of K support vector machines by using a one-to-many method so as to realize material classification of the fabric; wherein K is the material type of the fabric to be identified.
5. A fabric material identification method based on luminosity three-dimensional is characterized in that: the method using the identification system of any one of claims 1 to 4, comprising the steps of:
step one, image acquisition
Collecting a large number of fabric images, and taking a plurality of images of the same fabric at the same angle and in different light directions as one sample; labeling labels of the samples according to the material of the fabric, and enabling the samples to correspond to the labels one by one to be used as a training set;
step two, image processing
Calculating the reflectivity and the normal direction of each pixel point in the image according to the light direction information of the sample in the training set by using a light stereoscopic method, and collecting the normal direction to obtain a reflectivity map and a normal map of the three-dimensional shape of the sample;
step three, feature extraction
Inputting the reflectivity map and the normal map obtained in the second step into a VGG-M model, and outputting the feature vector of the corresponding sample by the VGG-M model;
step four, classification training
Aiming at samples of K different labels, carrying out classification training by using K support vector machines; defining 1 positive class of the support vector machine as a label, wherein the rest labels are negative classes of the support vector machine, and the positive class labels of the K support vector machines are not repeated; respectively inputting the feature vectors obtained in the third step into K support vector machines, and taking the class corresponding to the support vector machine with the largest classification value as a classification result of the sample;
step five, identifying the fabric
And (3) acquiring a fabric image which is not labeled and to be identified, processing according to the second step, inputting the fabric image into a VGG-M model for feature extraction, and inputting the fabric image into K support vector machines trained in the fourth step to obtain labels corresponding to the fabric, thereby completing identification.
6. The fabric material identification method based on luminosity three-dimensional technology as set forth in claim 5, wherein: at least 3 images in different light directions are acquired for the same fabric.
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| CN116740581B (en) * | 2023-08-16 | 2023-10-27 | 深圳市欢创科技有限公司 | Method for determining material identification model, method for returning to base station and electronic equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101069417A (en) * | 2004-11-30 | 2007-11-07 | 松下电器产业株式会社 | Image processing method, image processing apparatus, image processing program, and image file format |
| CN102095731A (en) * | 2010-12-02 | 2011-06-15 | 山东轻工业学院 | System and method for recognizing different defect types in paper defect visual detection |
| WO2021169209A1 (en) * | 2020-02-27 | 2021-09-02 | 平安科技（深圳）有限公司 | Method, apparatus and device for recognizing abnormal behavior on the basis of voice and image features |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101069417A (en) * | 2004-11-30 | 2007-11-07 | 松下电器产业株式会社 | Image processing method, image processing apparatus, image processing program, and image file format |
| CN102095731A (en) * | 2010-12-02 | 2011-06-15 | 山东轻工业学院 | System and method for recognizing different defect types in paper defect visual detection |
| WO2021169209A1 (en) * | 2020-02-27 | 2021-09-02 | 平安科技（深圳）有限公司 | Method, apparatus and device for recognizing abnormal behavior on the basis of voice and image features |
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