CN112330673A - Woven fabric density detection method based on image processing - Google Patents
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- 239000002759 woven fabric Substances 0.000 title claims abstract description 48
- 238000012545 processing Methods 0.000 title claims abstract description 30
- 238000001514 detection method Methods 0.000 title claims abstract description 13
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 36
- 230000008859 change Effects 0.000 claims abstract description 14
- 238000000034 method Methods 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000003702 image correction Methods 0.000 claims description 7
- 238000009499 grossing Methods 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 3
- 239000004744 fabric Substances 0.000 abstract description 17
- 230000009466 transformation Effects 0.000 description 10
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
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- 238000012937 correction Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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- 238000001303 quality assessment method Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 239000002356 single layer Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000004753 textile Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G06T3/04—
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- G06T5/70—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20064—Wavelet transform [DWT]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30124—Fabrics; Textile; Paper
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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 relates to a woven fabric density detection method based on image processing, which specifically comprises the following steps: 1. collecting image information of the woven fabric to be detected, and correcting the image; 2. performing multi-level wavelet transform processing on the corrected image, and drawing a gray characteristic change curve, wherein the level corresponding to the peak value of the relative gradient change value in the curve is the optimal decomposition level; 3. and calculating the warp density and weft density results of the fabric through the image components under the optimal decomposition level. The invention corrects the position of the woven fabric image to be detected before processing the image, and prevents the detection result from being influenced by the image inclination.
Description
Technical Field
The invention relates to the field of digital application of textiles, in particular to a woven fabric density detection method based on image processing.
Background
The density of the fabric and the arrangement of the density in the warp and weft directions have an important influence on the properties of the fabric, such as the weight, fastness, hand, water permeability, air permeability, etc., of the fabric, and thus are also important in the design of the fabric. Density is also an important physical indicator for fabric quality assessment. In the production of enterprises and factories, the traditional manual work is mostly adopted to detect the density of woven fabrics, the manual method is to observe and calculate the number of warp and weft yarns arranged in unit length by naked eyes through a cloth mirror, the method has strong subjectivity, wastes time and labor, is easy to generate larger human errors, and cannot well meet the industrial production requirements at the present situation.
Aiming at the problems of the traditional manual method, the modern computer image processing technology is provided to finish the work efficiently, simply and accurately. The computer measurement research on the warp and weft density of the woven fabric mostly adopts a Fourier transform method. If the Fourier transform technology is utilized to represent the frequency information in the spatial domain in a spectrogram, calculating frequency sealing points to measure the warp and weft density of the fabric; if the fabric image is subjected to FFT to obtain a frequency spectrum, the intensity and the distribution of the frequency spectrum are analyzed to calculate the stripe distribution and the period, so that the warp and weft density is obtained. Researches find that the stability and accuracy of Fourier transform are not enough, and the wavelet transform is further developed on the basis. If wavelet transformation is used for decomposing and reconstructing the woven fabric image, the warp and weft density of the fabric is automatically measured by calculation according to the reconstructed image; performing multilayer discrete wavelet decomposition and single-layer signal reconstruction on the woven fabric image by using wavelet transformation, respectively calculating the brightness average value of the reconstructed image along the warp and weft yarn directions, calculating the period of the reconstructed image according to the brightness signal, and further calculating the warp and weft density of the yarn; some patents are based on digital image processing technology and combine time-frequency transformation theory to realize computer detection of woven fabric density. The wavelet transformation has good processing effect, but some processing details need to be perfect, and automatic detection cannot be completely realized.
There are some methods for calculating the density of woven fabric by using wavelet transform, but how to determine the optimal decomposition level is generally tried and compared by a plurality of tests, and there is no relatively accurate and simple method for calculating the optimal decomposition level.
Disclosure of Invention
The invention aims to provide a woven fabric density detection method based on image processing, which has accurate detection result and high efficiency.
The technical scheme for solving the technical problems is as follows:
a woven fabric density detection method based on image processing comprises the following steps:
Further, the gray scale feature value calculation formula in step 2 is as follows:
where N represents the number of data for the component after decomposition, xiEach data in the decomposition is represented (i =1,2, …, N), k and c being constants.
Further, the image correction algorithm adopts a Randon transformation algorithm or a Hough transformation algorithm.
Further, before the number of the yarns is calculated in the step 3, the method further comprises the step of further carrying out binarization and smoothing optimization processing on the vertical component image and the horizontal component image to smooth the yarns so as to obtain clearly separated warp and weft yarn images.
The invention has the beneficial effects that: the invention corrects the position of the woven fabric image to be detected before processing the image, and prevents the detection result from being influenced by the image inclination.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is an image of a woven fabric to be detected;
FIG. 3 is a comparison of a corrected image and an original image;
FIG. 4 is a graph of gray scale characteristic variation;
FIG. 5 is a detail component diagram after decomposition and reconstruction;
FIG. 6 is an optimized warp and weft yarn image.
Detailed Description
The technical solution of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are only partial embodiments of the present invention, rather than full embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in figure 1, the method for detecting the density of the woven fabric comprises the steps of correcting a fabric image, drawing an energy image through multi-scale wavelet transformation of the fabric image, determining wavelet decomposition and reconstruction after a decomposition level, reconstructing, optimizing the image and calculating the density of warp and weft yarns.
The working principle of the invention is as follows: the method comprises the steps of compiling a woven fabric warp and weft density detection program, firstly collecting and obtaining a woven fabric image, carrying out image correction on the woven fabric image and intercepting fixed length, carrying out wavelet transformation processing on the corrected image and drawing a gray characteristic change image, determining a peak value decomposition grade according to a gray characteristic change curve and large data analysis, then carrying out wavelet decomposition and reconstruction to obtain a separated warp and weft density yarn image, calculating the number of warp and weft density yarns after optimizing the image, and calculating the warp and weft density of the fabric.
The detection working process of the invention is as follows:
the uniform equipment is adopted to collect and shoot images of the woven fabric, and the surface texture of the woven fabric is required to be clear, clean and free of stains. The fabric was kept as flat as possible when the image was collected, a fixed shooting height was taken, and a graduated scale was placed below the fabric, as shown in fig. 2.
Processing the acquired woven fabric image by using an image correction algorithm, wherein the calculation formula is as follows:
in a rectangular coordinate system, f (x, y) is a point on a line l, P is a distance from a coordinate origin to the line l, θ represents an included angle in a normal direction of the line l, and a linear equation is as follows:
，
the image correction algorithm can adopt a Randon transformation algorithm or a Hough transformation algorithm. If the image is an inclined image, correcting the image, if the image is a normal image, keeping the image unchanged, and after correction, enabling the texture of the woven fabric yarn to be horizontal, flat and vertical so as to obtain accurate and clear warp and weft density information of the woven fabric, and selecting and cutting a cloth sample diagram with the size of 5X5cm from the corrected image to store the cloth sample diagram for subsequent processing; the third image in fig. 3 is the final corrected image of fig. 2.
Carrying out multi-scale two-dimensional wavelet transform processing on the corrected image to obtain an approximate component, a horizontal detail component, a vertical detail component and a diagonal detail component, and calculating the latter three components by using a function to obtain the gray characteristic change of each component, wherein the function calculation formula is as follows:
n represents the number of data of the component after decomposition, xiEach data in the decomposition (i =1,2, …, N), k and c being constants, was plotted for its gray-scale-varying characteristic. As shown in FIG. 4, the corresponding vertical axis data reaches the lowest peak value when the number of decomposition levels is 3, and thus it is determined thatThe optimal decomposition level is 3;
therefore, the images of the vertical detail component and the horizontal detail component obtained in the 3 rd level decomposition represent the warp and weft yarn maps of the woven fabric. As shown in fig. 5, the wavelet decomposition and the reconstructed vertical component and horizontal component images are further subjected to binarization and smoothing optimization processing, so that the yarns are straightened, a smoothing effect is achieved, and clear and separated warp and weft yarn images are obtained. Calculating the number of warp and weft yarns in the image of fig. 6 by using a computer, namely calculating the number of pixel points of a vertical line (horizontal line) from top to bottom (from left to right), and dividing the pixel points by the width of the vertical (horizontal) image to obtain the yarn density in the vertical (horizontal) direction;
and calculating the weft density of the woven fabric by combining the computer resolution ppi and the yarn density sxmd in the vertical direction, wherein the calculated weft density of the woven fabric is as follows: w =153 roots/CM;
and the warp density of the woven fabric is obtained by the same calculation, and the warp density of the woven fabric is calculated in the example as follows: w =165 roots/CM.
Experimental data pairs are as follows:
according to the data, the calculation result of the method is basically accurate and can meet the requirement.
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, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A woven fabric density detection method based on image processing is characterized by comprising the following steps:
step 1, collecting image information of a woven fabric to be detected, and processing the collected woven fabric image by using an image correction algorithm to obtain a corrected image, so that warp yarns in the corrected image are in a vertical state, and weft yarns are in a horizontal state;
step 2,Subjecting the corrected image to multi-scale two-dimensional wavelet transform processing, obtaining an approximate component a1, a horizontal detail component, a vertical detail component and a diagonal detail component by 1 st-level decomposition, taking the horizontal detail component, the vertical detail component and the diagonal detail component as a detail component b1, and calculating a gray characteristic value of a1
Calculating the gray characteristic value of b1
Then 1 st level total eigenvalue
And performing multi-scale two-dimensional wavelet transform processing on the a1 again, obtaining an approximate component a2 and a detail component b2 by 2-level decomposition, and calculating a gray characteristic value of a2
Calculating the gray characteristic value of b2
Then 2 nd order total eigenvalue
When the ith level of the corrected image is decomposed, the ith level total characteristic value is
Respectively calculating the corresponding relative gradient change value of each stage
Wherein
Determining the decomposition by using the decomposition order as the horizontal axis and the corresponding relative gradient variation value as the vertical axisThe number of stages is corresponding to data points with different values, adjacent data points are connected by straight lines to form a continuous curve, a gray characteristic change curve is obtained, and the decomposition number corresponding to the peak value of the relative gradient change value in the gray characteristic change curve is the optimal decomposition number;
step 3, calculating the number of warps in the horizontal detail component corresponding to the optimal decomposition grade, namely the number of warps of the woven fabric to be detected, and calculating the warp density result of the woven fabric to be detected according to the width value of the woven fabric to be detected; and calculating the number of weft yarns in the vertical detail component corresponding to the optimal decomposition level, and calculating to obtain a weft density result of the woven fabric to be detected according to the length value of the woven fabric to be detected.
2. The method for detecting the density of the woven fabric based on the image processing as claimed in claim 1, wherein the gray scale feature value calculation formula in the step 2 is as follows:
where N represents the number of data for the component after decomposition, xiEach data in the decomposition is represented (i =1,2, …, N), k and c being constants.
3. The method for detecting the density of the woven fabric based on the image processing as claimed in claim 1, wherein the image correction algorithm adopts a Randon transform algorithm or a Hough transform algorithm.
4. The method for detecting the density of the woven fabric based on the image processing as claimed in claim 1, wherein before the number of the yarns is calculated in the step 3, the method further comprises a step of further performing binarization and smoothing optimization processing on the vertical component image and the horizontal component image to smooth the yarns so as to obtain clearly separated warp and weft yarn density images.
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Cited By (4)
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| CN113012105A (en) * | 2021-02-08 | 2021-06-22 | 武汉纺织大学 | Yarn hairiness detection and rating method based on image processing |
| CN113538489A (en) * | 2021-07-19 | 2021-10-22 | 武汉纺织大学 | Method for measuring fiber diameter of non-woven fabric |
| CN115266538A (en) * | 2022-09-30 | 2022-11-01 | 维柏思特衬布（南通）有限公司 | Woven belt water permeability detection device and method |
| CN113012105B (en) * | 2021-02-08 | 2024-04-26 | 武汉纺织大学 | Yarn hairiness detection and rating method based on image processing |
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| CN113538489A (en) * | 2021-07-19 | 2021-10-22 | 武汉纺织大学 | Method for measuring fiber diameter of non-woven fabric |
| CN113538489B (en) * | 2021-07-19 | 2022-09-09 | 武汉纺织大学 | Method for measuring fiber diameter of non-woven fabric |
| CN115266538A (en) * | 2022-09-30 | 2022-11-01 | 维柏思特衬布（南通）有限公司 | Woven belt water permeability detection device and method |
| CN115266538B (en) * | 2022-09-30 | 2022-12-09 | 维柏思特衬布（南通）有限公司 | Woven belt water permeability detection device and method |
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