CN109594319B - Intelligent detection device and method for warp and weft density of fabric - Google Patents
Intelligent detection device and method for warp and weft density of fabric Download PDFInfo
- Publication number
- CN109594319B CN109594319B CN201910011360.4A CN201910011360A CN109594319B CN 109594319 B CN109594319 B CN 109594319B CN 201910011360 A CN201910011360 A CN 201910011360A CN 109594319 B CN109594319 B CN 109594319B
- Authority
- CN
- China
- Prior art keywords
- fabric
- image
- density
- computer system
- imaging system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- D—TEXTILES; PAPER
- D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
- D06H—MARKING, INSPECTING, SEAMING OR SEVERING TEXTILE MATERIALS
- D06H3/00—Inspecting textile materials
- D06H3/08—Inspecting textile materials by photo-electric or television means
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Materials Engineering (AREA)
- Textile Engineering (AREA)
- Treatment Of Fiber Materials (AREA)
Abstract
The invention relates to an intelligent detecting device for the warp and weft density of a fabric, which comprises a computer system, an imaging system and a fabric conveying system, wherein the computer system is respectively connected with the imaging system and the fabric conveying system, and the fabric conveying system is used for smoothly conveying the fabric to be detected to an effective imaging area of the imaging system; the imaging system acquires a fabric image of a fabric to be detected in an effective imaging area, generates a fabric node digital matrix, and extracts related elements of the fabric node digital matrix according to a detection requirement to construct a knitting density detection line graph; the computer system generates a virtual variable constant grating pattern according to the size and the density detection range of the detected fabric, superimposes the knitting density detection line pattern and the virtual variable constant grating pattern to generate an interference fringe image, projects the interference fringe image, and determines the knitting density of the fabric according to the peak position of the projected image. The invention realizes the intelligent automatic detection of the density of the woven fabric.
Description
Technical Field
The invention relates to the technical field of fabric quality detection, in particular to an intelligent detection device and method for the warp and weft density of a fabric.
Background
As a traditional industry in China, the textile industry always occupies an important position in national economy. With the development of society and the improvement of living standard, the requirements of people on the quality of textiles are higher and higher, and the garment fabric is not a simple tool for covering a shy shelter in the past any more and becomes a mark for modern people to seek fashion. High-quality garment materials have strict requirements on fabric density, so the fabric yarn density becomes an important evaluation index of fabric quality. The traditional fabric density mainly depends on manual detection, and a detector counts the number of warps and wefts per inch through amplification equipment to obtain the warp and weft density of the fabric, or detects the warp and weft density of the fabric by using a fabric warp and weft density mirror. These methods require high concentration and patience of detection personnel, but long-time detection easily causes fatigue and looseness of personnel, thereby causing detection errors and detection failure caused by human factors. In the production of large-batch and high-requirement fabrics, the traditional manual detection method cannot meet the requirement of rapid and accurate detection of the quality of the fabrics.
Disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent detection device and method for the warp and weft density of a fabric, and realizing intelligent automatic detection of the weaving density of the fabric.
The technical scheme adopted by the invention for solving the technical problems is as follows: the intelligent detecting device for the warp and weft density of the fabric comprises a computer system, an imaging system and a fabric conveying system, wherein the computer system is respectively connected with the imaging system and the fabric conveying system, and the fabric conveying system is used for smoothly conveying the fabric to be detected to an effective imaging area of the imaging system; the imaging system acquires a fabric image of a fabric to be detected in an effective imaging area, generates a fabric node digital matrix, and extracts related elements of the fabric node digital matrix according to a detection requirement to construct a knitting density detection line graph; the computer system generates a virtual variable constant grating pattern according to the size and the density detection range of the detected fabric, superimposes the knitting density detection line pattern and the virtual variable constant grating pattern to generate an interference fringe image, projects the interference fringe image, and determines the knitting density of the fabric according to the peak position of the projected image.
The fabric conveying system comprises an active rolling wheel and a passive rolling wheel, the active rolling wheel and the passive rolling wheel are arranged in parallel, the active rolling wheel is connected with an output shaft of a motor, and an input end of a controller of the motor is connected with a computer system.
The imaging system comprises a light source, a lens and an image sensor, wherein the light source provides illumination for imaging of the imaging system from different directions, the lens is used for acquiring a fabric image, and the image sensor processes the acquired fabric image to obtain a knitting density detection line graph; the imaging system is placed in the light shield.
The image sensor carries out filtering and gray projection processing on the acquired fabric image to realize preliminary grid division on the fabric image; determining the grid color or gray value by a color clustering method, and adjusting the generated grid division; analyzing the edge strength information of the grid points, determining the weaving pattern of the fabric, and perfecting the weaving pattern by using the adjacent information and the color information of the yarns; each divided grid is regarded as a weaving node of the braided fabric, the weaving nodes are divided into warp nodes and weft nodes, and the warp nodes and the weft nodes are encoded by numbers 0 and 1 to obtain a fabric node number matrix; and then extracting the related elements of the fabric node digital matrix to generate a yarn density detection line by using the obtained arrangement characteristics of the elements of the fabric node digital matrix according to the detection requirements.
The method for generating the virtual variable constant raster image by the computer system comprises the following steps: setting the effective detection area size of fabric image as a x b pixel, the ratio of the physical size of fabric to the detected image as s pixel/inch, and the detected density range as [ f0～fn]Wherein f isiNumber of lines per inch; the size of a virtual variable constant grating pattern constructed by the computer system is a multiplied by b, and the scale of the grating can be expressed as f0,f1,f2,...,fi,...fn]The corresponding coordinates in the raster pattern are [0, b/n,2b/n,. ib/n,. and n, b/n](ii) a The grating curve is generated by the following equation, wherein y is ks/(x.n/b + f)0) And k takes the value: 1,2, (f)Na/s) representing different raster lines.
The technical scheme adopted by the invention for solving the technical problems is as follows: the intelligent detecting method for the warp and weft density of the fabric comprises the following steps:
(1) turning on a light source for illumination, focusing and calibrating an imaging system, generating a variable constant grating image according to the detection requirement of the detected fabric, and storing the variable constant grating image in a computer system;
(2) the computer system controls the fabric conveying system to convey the fabric to an effective imaging area of the imaging system at a constant speed and flatly;
(3) the imaging system captures an image of the fabric;
(4) carrying out graying and filtering pretreatment on the obtained fabric image, and then carrying out segmentation and attribute discrimination on knitting nodes of the knitted fabric by adopting a projection algorithm;
(5) classifying and coding all the weaving nodes to generate a digital matrix constructed by code elements 0 and 1;
(6) extracting matrix related elements to construct a knitting density detection line graph;
(7) according to the size of the obtained fabric image and the set detection range, the computer system generates a virtual variable constant grating image with the same size as the detection line image, and the virtual variable constant grating image is superposed with the weaving density detection line image to generate an interference fringe image;
(8) and projecting the interference fringe image in a direction perpendicular to the detection line, and determining the weaving density of the fabric according to the peak position of the projected image.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: the variable constant grating is automatically generated by a computer according to a detection object and a set detection range and according to set parameters, so that the variable constant grating is suitable for warp and weft detection of fabrics with various specifications and density ranges; the invention adopts the technical means of machine vision, image processing, mathematical modeling and the like, and can realize non-contact, automatic and intelligent high-efficiency weaving warp and weft density detection in the detection process. According to the invention, various detection lines reflecting the weaving quality can be generated by extracting the weaving matrix of the fabric, so that not only can the warp and weft density detection of the yarns be realized, but also the weaving density detection of other pattern lines can be realized.
Drawings
FIG. 1 is a schematic diagram of a detection system according to the present invention;
FIG. 2 is a side schematic view of the detection system of the present invention;
FIG. 3 is a weave matrix extraction diagram of the present invention;
FIG. 4 is a graph of the detection line generation of the present invention;
FIG. 5 is a density detection line graph constructed in accordance with the present invention;
FIG. 6 is a schematic diagram of a variable constant grating generated by the present invention;
FIG. 7 is a graph of interference fringes produced by the present invention;
FIG. 8 is a gray scale projection graph of interference fringes;
in the figure: the device comprises a computer 1, a display 2, an image sensor 3, a lens 4, a light source 5, a motor 6, a controller 7, an active rolling wheel 8, a passive rolling wheel 9, a light shield 10 and a fabric to be detected 11.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The embodiment of the invention relates to an intelligent detecting device for the warp and weft density of a fabric, which comprises a computer system, an imaging system and a fabric conveying system, wherein the computer system is respectively connected with the imaging system and the fabric conveying system, and the fabric conveying system is used for smoothly conveying a fabric 11 to be detected to an effective imaging area of the imaging system; the imaging system acquires a fabric image of a fabric to be detected in an effective imaging area, generates a fabric node digital matrix, and extracts related elements of the fabric node digital matrix according to a detection requirement to construct a knitting density detection line graph; the computer system generates a virtual variable constant grating pattern according to the size and the density detection range of the detected fabric, superimposes the knitting density detection line pattern and the virtual variable constant grating pattern to generate an interference fringe image, projects the interference fringe image, and determines the knitting density of the fabric according to the peak position of the projected image.
The fabric conveying system comprises an active rolling wheel 8 and a passive rolling wheel 9, the active rolling wheel 8 and the passive rolling wheel 9 are arranged in parallel, the active rolling wheel 8 is connected with an output shaft of a motor 6, and an input end of a controller 7 of the motor 6 is connected with a computer system.
The imaging system comprises two light sources 5, a lens 4 and an image sensor 3, wherein the two light sources 5 respectively provide illumination for imaging of the imaging system from two symmetrical directions, the lens 4 is used for acquiring a fabric image, and the image sensor 3 processes the acquired fabric image to obtain a knitting density detection line graph; the imaging system is placed in a light shield 10.
The fabric yarn density detection line graph is generated by processing a fabric image, extracting fabric weaving nodes, constructing a weaving digital matrix according to the types of the nodes and extracting related elements of the matrix according to detection requirements to construct a yarn density detection line graph.
The fabric surface image can be seen as formed by arranging interweaving points with different sizes, colors or gray scales according to a certain weaving structure, so that the weaving matrix can be used for representing the weaving node arrangement rule of the fabric. The acquired image is subjected to filtering and gray projection processing, and preliminary mesh division of the woven image can be realized. And determining the grid color or gray value by a color clustering method, and adjusting the generated grid division. The edge strength information at the grid points is then analyzed to determine the weave pattern of the fabric and refine it with yarn proximity information and color information. Each of the divided meshes is then further considered as an individual knitting node of the knit, the type of yarn node depending on the relative positions of the weft and warp yarns when interweaving. If the warp yarns are above, the stitch points are warp knuckles, and vice versa, weft knuckles. The warp and weft nodes are encoded with the numbers 0 and 1, respectively, and the whole detected fabric image can be expressed by the digital matrix of the fabric, and the matrix extraction process is shown in fig. 3. And then, according to the detection requirements, extracting matrix related elements by using the acquired arrangement characteristics of the weaving matrix elements to generate a density detection line of the yarn, wherein the thickness of the detection line is the same as that of the grating curve. FIG. 4 illustrates the generation of detection lines. Fig. 5 is a generated weft detection line map.
The computer system is composed of a computer 1 and a display 2, wherein the virtual variable constant grating generated by the computer 1 is automatically generated in the computer according to the detection range of the density of the detected yarns, the physical size of the detected fabric and the proportional relation of the generated image, and the size of the grating image is the same as the size of the generated detection line image.
The virtual variable constant grating curve is generated based on the following method: the virtual variable constant grating curve is generated based on the following method: setting the effective detection area size of fabric image as a x b pixel, the ratio of the physical size of fabric to the detected image as s pixel/inch, and the detected density range as [ f0～fn]Wherein f isiIn lines per inch (tpi). The computer constructed raster image has a size of a × b, and the scale of the raster can be expressed as f0,f1,f2,...,fi,...fn]The corresponding coordinates in the raster pattern are [0, b/n,2b/n,. ib/n,. and n, b/n](ii) a The grating curve is generated by the following equation, wherein k represents different grating lines. Ks/(x.n/b + f)0) And k is as follows: 1,2, (f)Na/s). The thickness of the drawn grating curve is half of the grating constant. The generated raster curve fills the non-equally spaced curve system of the raster image. The resulting raster image is shown in fig. 6.
The detection steps of the intelligent fabric warp and weft density detection device are as follows:
step 1, starting a system, turning on illumination of a light source, focusing and calibrating an imaging system, generating a variable constant grating image according to the detection requirement of the detected fabric, and storing the variable constant grating image in a computer system;
step 2, the computer system controls the fabric conveying system to convey the fabric to the effective imaging area of the system at a constant speed and flatly;
and 6, extracting matrix related elements to construct a knitting density detection line graph (see figure 5).
And 7, according to the size of the acquired fabric image and the set detection range, generating a virtual variable constant grating image with the size same as that of the detection line image by the computer, overlapping the virtual variable constant grating image with the knitting density detection line image, and generating an interference fringe image, wherein the image is shown in fig. 7.
And 8, projecting the interference image in a direction perpendicular to the detection line, and determining the weaving density of the fabric according to the peak position of the projected image, as shown in fig. 8.
It is not difficult to find that the variable constant grating in the invention is automatically generated by a computer according to the detection object and the set detection range and according to the set parameters, so the variable constant grating is suitable for the warp and weft detection of fabrics with various specifications and density ranges; the invention adopts the technical means of machine vision, image processing, mathematical modeling and the like, and can realize non-contact, automatic and intelligent high-efficiency weaving warp and weft density detection in the detection process. According to the invention, various detection lines reflecting the weaving quality can be generated by extracting the weaving matrix of the fabric, so that not only can the warp and weft density detection of the yarns be realized, but also the weaving density detection of other pattern lines can be realized.
Claims (6)
1. An intelligent detecting device for the warp and weft density of a fabric comprises a computer system, an imaging system and a fabric conveying system, wherein the computer system is respectively connected with the imaging system and the fabric conveying system; the imaging system acquires a fabric image of a fabric to be detected in an effective imaging area, generates a fabric node digital matrix, and extracts related elements of the fabric node digital matrix according to a detection requirement to construct a knitting density detection line graph; the computer system generates a virtual variable constant grating pattern according to the size and the density detection range of the detected fabric, superimposes the knitting density detection line pattern and the virtual variable constant grating pattern to generate an interference fringe image, projects the interference fringe image, and determines the knitting density of the fabric according to the peak position of the projected image.
2. The device for intelligently detecting the thread count and the thread count of the fabric according to claim 1, wherein the fabric conveying system comprises an active rolling wheel and a passive rolling wheel, the active rolling wheel and the passive rolling wheel are arranged in parallel, the active rolling wheel is connected with an output shaft of a motor, and an input end of a controller of the motor is connected with a computer system.
3. The intelligent fabric warp and weft density detection device according to claim 1, wherein the imaging system comprises a light source, a lens and an image sensor, the light source provides illumination for imaging of the imaging system from different directions, the lens is used for acquiring a fabric image, and the image sensor processes the acquired fabric image to obtain a weaving density detection line graph; the imaging system is placed in the light shield.
4. The intelligent fabric warp and weft density detection device according to claim 3, wherein the image sensor performs filtering and gray projection processing on the acquired fabric image to realize preliminary mesh division on the fabric image; determining the grid color or gray value by a color clustering method, and adjusting the generated grid division; analyzing the edge strength information of the grid points, determining the weaving pattern of the fabric, and perfecting the weaving pattern by using the adjacent information and the color information of the yarns; each divided grid is regarded as a weaving node of the braided fabric, the weaving nodes are divided into warp nodes and weft nodes, and the warp nodes and the weft nodes are encoded by numbers 0 and 1 to obtain a fabric node number matrix; and then extracting the related elements of the fabric node digital matrix to generate a yarn density detection line by using the obtained arrangement characteristics of the elements of the fabric node digital matrix according to the detection requirements.
5. The device of claim 1, wherein the computer system generates the virtual variable constant raster pattern by: setting the effective detection area size of fabric image as a x b pixel, the ratio of the physical size of fabric to the detected image as s pixel/inch, and the detected density range as [ f0～fn]Wherein f isiNumber of lines per inch; the size of a virtual variable constant grating pattern constructed by the computer system is a multiplied by b, and the scale of the grating can be expressed as f0,f1,f2,…,fi,…fn]The coordinates of the grating pattern are [0, b/n,2b/n, … ib/n, …, nb/n](ii) a The grating curve is generated by the following equation, wherein y is ks/(x.n/b + f)0) And k takes the value: k is 1,2, …, (f)Na/s) representing different raster lines.
6. An intelligent detecting method for the thread count of the fabric, which is characterized in that the intelligent detecting device for the thread count of the fabric according to any claim from 1 to 5 is adopted, and comprises the following steps:
(1) turning on a light source for illumination, focusing and calibrating an imaging system, generating a variable constant grating image according to the detection requirement of the detected fabric, and storing the variable constant grating image in a computer system;
(2) the computer system controls the fabric conveying system to convey the fabric to an effective imaging area of the imaging system at a constant speed and flatly;
(3) the imaging system captures an image of the fabric;
(4) carrying out graying and filtering pretreatment on the obtained fabric image, and then carrying out segmentation and attribute discrimination on knitting nodes of the knitted fabric by adopting a projection algorithm;
(5) classifying and coding all the weaving nodes to generate a digital matrix constructed by code elements 0 and 1;
(6) extracting matrix related elements to construct a knitting density detection line graph;
(7) according to the size of the obtained fabric image and the set detection range, the computer system generates a virtual variable constant grating image with the same size as the detection line image, and the virtual variable constant grating image is superposed with the weaving density detection line image to generate an interference fringe image;
(8) and projecting the interference fringe image in a direction perpendicular to the detection line, and determining the weaving density of the fabric according to the peak position of the projected image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910011360.4A CN109594319B (en) | 2019-01-07 | 2019-01-07 | Intelligent detection device and method for warp and weft density of fabric |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910011360.4A CN109594319B (en) | 2019-01-07 | 2019-01-07 | Intelligent detection device and method for warp and weft density of fabric |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109594319A CN109594319A (en) | 2019-04-09 |
| CN109594319B true CN109594319B (en) | 2021-08-10 |
Family
ID=65964959
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910011360.4A Active CN109594319B (en) | 2019-01-07 | 2019-01-07 | Intelligent detection device and method for warp and weft density of fabric |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109594319B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110241602B (en) * | 2019-05-31 | 2021-06-01 | 长安大学 | Spiral grating density disc and method for measuring warp and weft density of fabric |
| CN110687116B (en) * | 2019-10-15 | 2022-03-25 | 安顺学院 | Fabric warp-weft density mirror measuring device and method |
| CN110670331B (en) * | 2019-11-19 | 2022-03-22 | 绍兴柯桥浙工大创新研究院发展有限公司 | Fabric perching equipment |
| CN111208133A (en) * | 2020-01-14 | 2020-05-29 | 北京交通大学 | Fabric density measuring method |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202583071U (en) * | 2012-01-06 | 2012-12-05 | 青岛检验检疫技术发展中心 | Warp-and-weft density automatic analyzer for fabric |
| CN102788792A (en) * | 2012-03-31 | 2012-11-21 | 江南大学 | Device for measuring density of weft knitted fabric based on image analysis |
| CN103234969B (en) * | 2013-04-12 | 2015-03-04 | 江苏大学 | Method for measuring fabric weft density based on machine vision |
| CN103604937B (en) * | 2013-11-22 | 2015-06-10 | 青岛大学 | Fabric analysis system and method based on biaxial mechanical stretch processing |
| CN104778698B (en) * | 2015-04-13 | 2018-05-08 | 佛山市南海天富科技有限公司 | A kind of fabric weft density measurement method and equipment |
| CN105300319B (en) * | 2015-11-20 | 2017-11-07 | 华南理工大学 | A kind of quick three-dimensional stereo reconstruction method based on chromatic grating |
| KR102131480B1 (en) * | 2016-07-19 | 2020-07-07 | 장병규 | Inspection device for fabric |
| CN107655893A (en) * | 2017-11-15 | 2018-02-02 | 佛山鑫进科技有限公司 | A kind of Density COMPUTER DETECTION device |
-
2019
- 2019-01-07 CN CN201910011360.4A patent/CN109594319B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109594319A (en) | 2019-04-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109594319B (en) | Intelligent detection device and method for warp and weft density of fabric | |
| Zhu et al. | Yarn-dyed fabric defect detection based on autocorrelation function and GLCM | |
| Kang et al. | Automatic recognition of fabric weave patterns by digital image analysis | |
| CN105550660B (en) | A kind of recognition methods of woven fabric structure structure species | |
| Zhang et al. | A review of fabric identification based on image analysis technology | |
| CN111127383A (en) | Digital printing online defect detection system and implementation method thereof | |
| CN103604809A (en) | Pattern cloth flaw online visual inspection method | |
| Pan et al. | Automatic detection of the layout of color yarns for yarn-dyed fabric via a FCM algorithm | |
| CN109211918B (en) | Fabric bow weft detection method based on weft trend | |
| Jeong et al. | Applying image analysis to automatic inspection of fabric density for woven fabrics | |
| US6987867B1 (en) | Process for evaluating data from textile fabrics | |
| Banumathi et al. | Fabric inspection system using artificial neural networks | |
| Hu | Computer technology for textiles and apparel | |
| Li et al. | A novel digital method for weave pattern recognition based on photometric differential analysis | |
| CN113936001B (en) | Textile surface flaw detection method based on image processing technology | |
| Lim et al. | Analysis of woven fabric structure using image analysis and artificial intelligence | |
| CN109919028B (en) | Flexible coordinate system establishing and shape identifying method based on fabric weave structure | |
| CN110672209A (en) | Online detection method for warp knitting cloth color difference | |
| Pan et al. | Color clustering analysis of yarn-dyed fabric in HSL color space | |
| Techniková et al. | Pilling evaluation of patterned fabrics based on a gradient field method | |
| JP2008089534A (en) | Method and device for inspecting fabric of carbon fiber | |
| Owczarek | The impact and importance of fabric image preprocessing for the new method of individual inter-thread pores detection | |
| Wu et al. | Design method of fabric density sensor based on the virtual grating with gradual constant | |
| CN106757649B (en) | A kind of system and method that jacquard fabrics line version replicates automatically | |
| CN114782426A (en) | Knitted fabric broken yarn defect detection method based on artificial intelligence system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |