Method and device for detecting yarn evenness in high-speed spinning

Abstract

The present invention discloses a method and device for detecting yarn evenness for high-speed spinning. The method comprises the following steps: capturing an image of a high-speed moving yarn under backlight conditions; extracting energy information from the image indicating that the yarn blocks the light source, converting the energy information into yarn width values ​​for statistical analysis, thereby obtaining the yarn diameter; and determining whether the yarn defect needs to be removed based on the yarn diameter and the length of the yarn defect, thereby detecting yarn evenness. This method achieves rapid detection of yarn evenness suitable for high-speed spinning, and has good real-time performance and low manufacturing cost, making it suitable for large-scale promotion and use.

Description

Technical Field

[0001] The present invention relates to the field of yarn detection, in particular to a method and device for detecting yarn evenness of high-speed spinning. Background Art

[0002] Yarn quality and yarn performance determine the quality of textiles, and yarn evenness is key to evaluating these qualities. Yarns with poor evenness can reduce yarn strength and lead to high breakage rates. During the spinning process, higher spinning speeds increase the risk of uneven yarn evenness, making yarn evenness testing particularly important.

[0003] The yarn evenness detection methods mainly include capacitance method, photoelectric method, cutting weighing method, etc.

[0004] The capacitance method uses two perpendicular capacitor plates to measure yarn running at a constant speed. Because yarn has a greater nodal coefficient than air, the presence of yarn between the plates increases the capacitance between them. The capacitance is used to calculate yarn evenness. However, this method is affected by factors such as the yarn blend ratio and the test environment, resulting in relatively low accuracy.

[0005] The photoelectric method uses a diffuse light source placed on one side of the moving yarn and a photoelectric sensor (such as a silicon photovoltaic panel) on the other side. The energy from the yarn blocking the light is converted into a sensor signal, which reflects the diameter of the yarn within the detection area. This method is easily affected by dust, yarn vibration, etc.

[0006] The cut-off weighing method uses a yarn length measuring instrument to automatically wind fixed lengths of yarn under a certain tension. The yarn is then weighed individually using an electronic balance. The weight of each yarn segment is analyzed to determine the yarn weight unevenness and coefficient of variation, thereby determining yarn evenness. This method is only suitable for unevenness testing of long yarn segments and is cumbersome, time-consuming, and labor-intensive.

[0007] With the rapid development of computer control technology, image processing technology based on embedded systems has been widely used in yarn defect detection. Currently, my country's textile industry is in the process of transitioning from traditional spinning to digital spinning, and yarn quality inspection methods using machine vision are gradually being adopted by the industry. Embedded systems based on the STM32 offer advantages such as low cost and high processing power, making their application in yarn evenness detection in high-speed spinning possible.

[0008] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the Invention

[0009] The purpose of the present invention is to provide a method and device for detecting yarn evenness of high-speed spinning, so as to solve the problem that the existing technology has relatively low detection accuracy due to the influence of yarn blending ratio, test environment, dust, yarn jitter, etc.

[0010] To achieve the above objectives, an embodiment of the present invention provides a method for detecting yarn evenness in high-speed spinning.

[0011] In one or more embodiments of the present invention, the method includes: collecting images of high-speed moving yarns under backlight conditions; extracting energy information of the yarn blocking the light source in the image, and converting the energy information into yarn width values ​​for statistics, thereby obtaining the diameter of the yarn; and judging whether the yarn defect needs to be eliminated based on the yarn diameter and the yarn defect length, so as to achieve yarn evenness detection.

[0012] In one or more embodiments of the present invention, the energy information of the yarn blocking the light source in the image is extracted, and the energy information is converted into the yarn width value for statistics, including: scanning the yarn image pixel by pixel column, extracting the energy information of the yarn blocking the light source of a single column of pixels based on the sudden change of the grayscale value of the pixels in the same column of the image, so as to obtain the grayscale value of the current yarn pixel; starting from the second column of pixels of the yarn image, determining the starting pointer of the boundary of the yarn in the current column according to the boundary position of the yarn in the previous column; and removing the background interference in the energy information from the grayscale value of the pixel in the i-th column and the j-th row, converting the pixel grayscale value into the width value of the current yarn and performing statistics to obtain the diameter of the yarn.

[0013] In one or more embodiments of the present invention, whether the yarn defect needs to be removed is determined based on the yarn diameter and the yarn defect length, including: determining whether the yarn starting from the current column has a yarn defect based on the yarn diameter; if so, counting the duration of the yarn defect, analyzing the type of the yarn defect based on the counted duration of the yarn defect, and determining whether the yarn defect needs to be removed.

[0014] In one or more embodiments of the present invention, the method also includes: using a fast edge detection method to extract the yarn length, and calculating the difference in yarn edge pixels, and using the difference as a reference threshold for the sudden change in the grayscale value of the pixels in the same column; and recording the maximum grayscale value of the yarn and the average grayscale value of the yarn background, and using the maximum grayscale value and the average grayscale value of the background as parameters for calculating the yarn diameter.

[0015] In one or more embodiments of the present invention, determining the starting pointer of the boundary of the current column of yarn strands based on the boundary position of the previous column of yarn strands includes: calculating the initial value of the starting pointer of the boundary of the i+1th column of yarn strands based on the boundary position of the i-th column of yarn strands and the pointer offset.

[0016] In one or more embodiments of the present invention, removing background interference in the energy information from the grayscale values ​​of the pixels in the i-th column and j-th row, converting the pixel grayscale values ​​into the width values ​​of the current yarn and performing statistics to obtain the diameter of the yarn, includes: calculating the width value of the current i-th column and j-th row yarn pixels based on the background average grayscale value and the grayscale value of the current i-th column and j-th row yarn pixels; accumulating the width values ​​of the yarn pixels from the upper pixel boundary to the lower pixel boundary of the i-th column yarn to obtain the total width value of the i-th column yarn; and calculating the i-th column yarn diameter based on the i-th column yarn total width value, the maximum grayscale value, the background average grayscale value, the yarn length in the image, and the size of the image.

[0017] In one or more embodiments of the present invention, the duration of the yarn defect is counted, the type of the yarn defect is analyzed based on the counted duration of the yarn defect, and it is determined whether the yarn defect needs to be eliminated, including: when the diameter of the yarn in the i-th column is greater than a preset yarn defect threshold, the current number of yarn defect duration columns is accumulated to obtain a total number of duration columns, and the total number of duration columns is converted into an actual yarn defect length; when the actual yarn defect length is greater than the preset yarn defect duration threshold, the yarn defect is eliminated.

[0018] In another aspect of the present invention, a device for detecting yarn evenness in high-speed spinning is provided, which includes a collection module, a calculation module and a judgment module.

[0019] The acquisition module is used to capture images of high-speed moving yarn under backlight conditions.

[0020] The calculation module is used to extract the energy information of the yarn blocking the light source in the image, and convert the energy information into yarn width values ​​for statistics, so as to obtain the diameter of the yarn.

[0021] The judgment module is used to judge whether the yarn defect needs to be removed according to the yarn diameter and the yarn defect length, so as to realize the uniformity detection of the yarn evenness.

[0022] In one or more embodiments of the present invention, the calculation module is also used to: scan the yarn image pixel by pixel column, extract the energy information of the yarn blocking the light source of a single column of pixels based on the sudden change in the grayscale value of the pixels in the same column of the image, so as to obtain the grayscale value of the current yarn pixel; starting from the second column of pixels of the yarn image, determine the starting pointer of the boundary of the current column of yarn strands according to the boundary position of the previous column of yarn strands; and remove the background interference in the energy information from the grayscale value of the pixel in the i-th column and the j-th row, convert the pixel grayscale value into the width value of the current yarn and perform statistics to obtain the diameter of the yarn.

[0023] In one or more embodiments of the present invention, the judgment module is further used to: determine whether the yarn starting from the current column has a yarn defect based on the yarn diameter; if so, count the duration of the yarn defect, analyze the type of the yarn defect based on the counted duration of the yarn defect, and determine whether the yarn defect needs to be removed.

[0024] In one or more embodiments of the present invention, the calculation module is also used to: use a fast edge detection method to extract the yarn length, and calculate the difference between the yarn edge pixels, and use the difference as the reference threshold for the sudden change in the grayscale value of the pixels in the same column; and record the maximum grayscale value of the yarn and the average grayscale value of the yarn background, and use the maximum grayscale value and the average grayscale value of the background as parameters for calculating the yarn diameter.

[0025] In one or more embodiments of the present invention, the calculation module is further configured to calculate an initial value of a starting pointer of the (i+1)th column of yarn strand boundary according to the position of the yarn strand boundary of the i-th column and the pointer offset.

[0026] In one or more embodiments of the present invention, the calculation module is also used to: calculate the width value of the yarn pixel in the current i-th column and j-th row based on the average grayscale value of the background and the grayscale value of the yarn pixel in the current i-th column and j-th row; accumulate the width values ​​of the yarn pixels from the upper pixel boundary to the lower pixel boundary of the yarn line in the i-th column to obtain the total width value of the yarn in the i-th column; and calculate the diameter of the yarn in the i-th column based on the total width value of the yarn in the i-th column, the maximum grayscale value, the average grayscale value of the background, the yarn length in the image, and the size of the image.

[0027] In one or more embodiments of the present invention, the judgment module is also used to: when the diameter of the yarn in the i-th column is greater than a preset yarn defect threshold, accumulate the current number of continuous columns of yarn defects to obtain a total number of continuous columns, and convert the total number of continuous columns into an actual yarn defect length; when the actual yarn defect length is greater than a preset yarn defect duration threshold, eliminate the yarn defect.

[0028] Compared with the existing technology, the method and device for detecting yarn evenness of high-speed spinning according to the embodiment of the present invention can extract the energy information of the light source blocked by the yarn in the image and convert it into the corresponding yarn width value, thereby obtaining the yarn diameter of the column of pixels; judge the type of yarn defects according to the yarn diameter and continuous length, and control the yarn defect removal mechanism to remove the yarn defects, thereby realizing rapid detection of yarn evenness suitable for high-speed spinning, and the method has good real-time performance and low manufacturing cost, which is conducive to large-scale promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of a method for detecting yarn evenness of high-speed spinning according to one embodiment of the present invention;

[0030] Figure 2 2 is a schematic structural diagram of a method for detecting yarn evenness of high-speed spinning according to an embodiment of the present invention;

[0031] Figure 3 is a specific flow chart of a method for detecting yarn evenness of high-speed spinning according to one embodiment of the present invention;

[0032] Figure 4 4 is a structural diagram of a device for detecting yarn evenness of high-speed spinning according to one embodiment of the present invention. DETAILED DESCRIPTION

[0033] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0034] Unless expressly stated otherwise, throughout the specification and claims, the term "comprise" or variations such as "include" or "comprising", etc., will be understood to include the stated elements or components but not to exclude other elements or other components.

[0035] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0036] Example 1

[0037] like Figures 1 to 3 As shown, a method for detecting yarn evenness of high-speed spinning in one embodiment of the present invention is introduced, and the method includes the following steps.

[0038] In step S101, an image of a high-speed moving yarn is captured under backlight conditions.

[0039] like Figure 2As shown, under backlight illumination conditions, the STM32 control chip controls the vision sensor MT9V034 to collect images of high-speed moving yarns. The image size is X×Y, and the length of the yarn in the image is H.

[0040] In step S102, the energy information of the light source blocked by the yarn in the image is extracted, and the energy information is converted into a yarn width value for statistics, so as to obtain the diameter of the yarn.

[0041] Perform a column-by-column pixel scan on the yarn image collected by the vision sensor MT9V034, and use the sudden change of the gray-scale values of the pixels in the same column of the image (i.e., the image edge) to complete the extraction of the energy of the yarn blocking the light source in a single column of pixels, that is, the extraction of the yarn evenness, so as to obtain the gray-scale value of the current yarn pixel. Among them, use the fast edge detection method to complete the extraction of the yarn evenness, and calculate the difference between the yarn edge pixels, and use the difference between the yarn edge pixels as the reference threshold for the sudden change of the gray-scale values of the pixels in the same column.

[0042] To reduce the detection time of the yarn evenness, starting from the second column of pixels in the yarn image, determine the starting pointer of the current column of yarn evenness boundary according to the boundary position of the previous column of yarn evenness. Specifically, during the determination of the yarn evenness boundary, according to the boundary position k i of the yarn evenness in the i-th column and the pointer offset a, calculate the initial value c i+1 of the starting pointer of the yarn evenness boundary in the (i + 1)-th column. Among them, the higher the pointer offset a, the better the system robustness; the lower the pointer offset a, the faster the system detection speed.

[0043] c i+1 = k i - a

[0044] After determining the yarn evenness boundary, remove the background interference from the gray-scale value of the pixel in the i-th column and the j-th row (i < X, j < Y) in the energy information of the yarn blocking the light source, and convert the gray-scale value of the pixel in the i-th column and the j-th row after removing the interference into the width value of the current yarn for statistics, and record the maximum gray-scale value P max of the yarn and the average gray-scale value P background of the yarn background, so as to obtain the parameters for calculating the yarn diameter.

[0045] Specifically, after determining the boundary, perform statistics on the yarn evenness. According to the average gray-scale value P background of the background and the gray-scale value P (i,j) of the yarn pixel in the current i-th column and the j-th row, calculate the width value m (i,j) represented by the yarn pixel in the current i-th column and the j-th row:

[0046] m (i,j) = P Background - p (i,j) .

[0047] Set the pixel upper boundary u of the yarn strand in column i i To the lower pixel boundary v i The yarn pixel represents the width value m (i,j) Accumulate and obtain the total width M of the yarn in column i i :

[0048]

[0049] According to the recorded maximum gray value P of the yarn max , combined with the yarn length H in the image and the yarn image size X×Y, calculate the yarn diameter d of the i-th column i :

[0050]

[0051] In step S103, it is determined whether the yarn defects need to be removed according to the yarn diameter and the yarn defect length, so as to realize the yarn evenness detection.

[0052] The STM32 control chip determines whether the yarn starting from the current column is a yarn defect based on the yarn diameter. If so, it counts the duration of the yarn defect, analyzes the type of yarn defect based on the statistical duration of the yarn defect, and determines whether the yarn defect needs to be removed.

[0053] Specifically, when the diameter of the yarn in the i-th column is d i When the yarn defect length is greater than the preset yarn defect threshold, the current number of continuous columns of yarn defects is accumulated to obtain the total number of continuous columns I, and the total number of continuous columns is converted into the actual yarn defect length l according to the yarn length and yarn image size:

[0054]

[0055] When the actual yarn defect length l is greater than the preset yarn defect duration threshold L, the yarn defect is removed, thereby achieving yarn evenness detection.

[0056] like Figure 4 As shown, a device for detecting yarn evenness of high-speed spinning according to a specific embodiment of the present invention is introduced.

[0057] In an embodiment of the present invention, the apparatus for detecting yarn evenness of high-speed spinning includes a collection module 401 , a calculation module 402 and a judgment module 403 .

[0058] The acquisition module 401 is used to acquire images of high-speed moving yarn under backlight conditions.

[0059] The calculation module 402 is used to extract the energy information of the yarn blocking the light source in the image, and convert the energy information into yarn width values ​​for statistics, so as to obtain the yarn diameter.

[0060] The judgment module 403 is used to judge whether the yarn defects need to be removed according to the yarn diameter and the yarn defect length, so as to realize the uniformity detection of the yarn evenness.

[0061] The calculation module 402 is also used to: scan the yarn image pixel by pixel column, extract the energy information of the yarn blocking the light source of a single column of pixels based on the sudden change of the grayscale value of the pixels in the same column of the image, so as to obtain the grayscale value of the current yarn pixel; starting from the second column of pixels of the yarn image, determine the starting pointer of the boundary of the yarn in the current column according to the boundary position of the yarn in the previous column; and remove the background interference in the energy information of the grayscale value of the pixel in the i-th column and the j-th row, convert the pixel grayscale value into the width value of the current yarn and perform statistics to obtain the diameter of the yarn.

[0062] The judgment module 403 is further used to: judge whether the yarn starting from the current column has a yarn defect based on the yarn diameter; if so, count the duration of the yarn defect, analyze the type of the yarn defect based on the counted duration of the yarn defect, and judge whether the yarn defect needs to be removed.

[0063] The calculation module 402 is also used to: use the fast edge detection method to extract the yarn evenness, and calculate the difference of the yarn edge pixels, and use the difference as the reference threshold for the sudden change of the grayscale value of the pixels in the same column; and record the maximum grayscale value of the yarn and the average grayscale value of the yarn background, and use the maximum grayscale value and the average grayscale value of the background as parameters for calculating the yarn diameter.

[0064] The calculation module 402 is further configured to calculate an initial value of a starting pointer of the (i+1)th column of yarn strand boundaries according to the position of the yarn strand boundary of the i-th column and the pointer offset.

[0065] The calculation module 402 is also used to: calculate the width value of the yarn pixel in the current i-th column and j-th row based on the average grayscale value of the background and the grayscale value of the yarn pixel in the current i-th column and j-th row; accumulate the width values ​​of the yarn pixels from the upper pixel boundary to the lower pixel boundary of the yarn line in the i-th column to obtain the total width value of the yarn in the i-th column; and calculate the diameter of the yarn in the i-th column based on the total width value of the yarn in the i-th column, the maximum grayscale value, the average grayscale value of the background, the yarn length in the image, and the size of the image.

[0066] The judgment module 403 is also used to: when the diameter of the yarn in the i-th column is greater than the preset yarn defect threshold, accumulate the current number of continuous columns of yarn defects to obtain the total number of continuous columns, and convert the total number of continuous columns into the actual yarn defect length; when the actual yarn defect length is greater than the preset yarn defect duration threshold, remove the yarn defect.

[0067] According to the method and device for detecting yarn evenness of high-speed spinning according to the embodiment of the present invention, it is possible to extract the energy information of the light source blocked by the yarn in the image and convert it into the corresponding yarn width value, thereby obtaining the yarn diameter of the column of pixels; determine the type of yarn defect according to the yarn diameter and the continuous length, and control the yarn defect removal mechanism to remove the yarn defect, thereby realizing the rapid detection of yarn evenness suitable for high-speed spinning, and the method has good real-time performance and low manufacturing cost, which is conducive to large-scale promotion and use.

[0068] The present invention is described with reference to flowcharts and / or block diagrams of methods and devices (systems) according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0069] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0071] The foregoing descriptions of specific exemplary embodiments of the present invention are for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many variations and modifications are possible in light of the foregoing teachings. The exemplary embodiments have been selected and described for the purpose of explaining the specific principles of the invention and their practical application, thereby enabling those skilled in the art to realize and utilize a variety of exemplary embodiments of the invention and various options and modifications. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for detecting yarn evenness of high-speed spinning, characterized in that: The method comprises: Under backlight conditions, images of high-speed moving yarns are collected; Extracting energy information of the light source blocked by the yarn in the image, and converting the energy information into yarn width values ​​for statistical analysis to obtain the yarn diameter; and Determining whether the yarn defect needs to be removed based on the yarn diameter and the yarn defect length, so as to achieve yarn evenness detection; The extracting of energy information of the yarn blocking the light source in the image and converting the energy information into yarn width values ​​for statistical analysis includes: scanning the yarn image pixel by pixel column, extracting the energy information of the yarn blocking the light source in a single column of pixels based on a sudden change in the grayscale value of the pixels in the same column of the image, so as to obtain the grayscale value of the current yarn pixel; starting from the second column of pixels in the yarn image, determining the starting pointer of the boundary of the yarn stalk in the current column based on the boundary position of the yarn stalk in the previous column; and removing background interference from the energy information from the grayscale value of the pixel in the i-th column and the j-th row, converting the pixel grayscale value into the width value of the current yarn and performing statistical analysis to obtain the diameter of the yarn; The method of determining the starting pointer of the boundary of the current column of yarns according to the boundary position of the previous column of yarns comprises: determining the starting pointer of the boundary of the current column of yarns according to the boundary position of the i-th column of yarns Calculate the initial value of the starting pointer of the i+1th column yarn boundary using the pointer offset a ; The method of removing background interference from the energy information of the pixel grayscale values ​​in the i-th column and the j-th row, converting the pixel grayscale values ​​into the width value of the current yarn and performing statistics to obtain the diameter of the yarn includes: removing background interference from the energy information of the yarn blocking the light source from the pixel grayscale values ​​in the i-th column and the j-th row, converting the pixel grayscale values ​​in the i-th column and the j-th row after removing the interference into the width value of the current yarn and performing statistics, and recording the maximum grayscale value of the yarn and the average gray value of yarn background , thus obtaining the parameters for calculating the yarn diameter; after determining the boundary, the yarn evenness is statistically analyzed, and the average gray value of the background is used to calculate the yarn diameter. and the grayscale value of the yarn pixel in the current i-th column and j-th row , calculate the width value represented by the yarn pixel in the current i-th column and j-th row :The pixel upper boundary of the i-th column yarn To the bottom pixel boundary The width of the yarn represented by pixels Accumulate and obtain the total width of the yarn in column i ; According to the maximum gray value of the yarn recorded , combined with the yarn length H in the image and the yarn image size X×Y, calculate the yarn diameter of the i-th column .

2. The method for detecting yarn evenness of high-speed spinning according to claim 1, wherein: Determining whether the yarn defect needs to be removed according to the yarn diameter and the yarn defect length includes: Determine whether the yarn starting from the current column has a yarn defect according to the yarn diameter; if so, The duration of the yarn defect is counted, the type of the yarn defect is analyzed according to the counted duration of the yarn defect, and it is determined whether the yarn defect needs to be removed.

3. The method for detecting yarn evenness of high-speed spinning according to claim 1, wherein: The method further comprises: The yarn evenness is extracted by using a fast edge detection method, and the pixel difference of the yarn edge is calculated, and the difference is used as a reference threshold for the sudden change of the grayscale value of the pixels in the same column.

4. The method for detecting yarn evenness of high-speed spinning according to claim 1, wherein: The duration of the yarn defect is counted, the type of the yarn defect is analyzed according to the counted duration of the yarn defect, and whether the yarn defect needs to be removed is determined, including: When the diameter of the yarn in the i-th column is greater than a preset yarn defect threshold, the number of continuous columns of the current yarn defect is accumulated to obtain a total number of continuous columns, and the total number of continuous columns is converted into the actual yarn defect length; When the actual yarn defect length is greater than a preset yarn defect duration threshold, the yarn defect is removed.

5. A device for detecting yarn evenness of high-speed spinning, applied to the method for detecting yarn evenness of high-speed spinning according to any one of claims 1 to 4, characterized in that: The device comprises: An acquisition module, used for acquiring images of high-speed moving yarn under backlight conditions; a calculation module, configured to extract energy information of the light source blocked by the yarn in the image, and convert the energy information into yarn width values ​​for statistical analysis, thereby obtaining the diameter of the yarn; and The judgment module is used to judge whether the yarn defect needs to be removed according to the yarn diameter and the yarn defect length, so as to realize the uniformity detection of the yarn evenness.

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