CN108664588B - Automatic method for online detection and control of cloth skewing - Google Patents
Automatic method for online detection and control of cloth skewing Download PDFInfo
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Abstract
The invention discloses an automatic method for detecting and controlling cloth skewness on line, which judges clear color cloth and pattern cloth through a cloth model distinguishing process so as to correspondingly extract characteristic data of the current cloth, and then compares the characteristic data with a database to obtain a cloth model, so that a weft straightening control signal generating process obtains skewness requirement data corresponding to the current cloth model in the database according to the current cloth model, has outstanding substantive characteristics and obvious progress, can save the workload of manually setting the skewness requirement data and/or the weft skewness requirement data, and is suitable for the situation that different types of cloth are sewed together for continuous conveying; the control method has the advantages that the width boundary of the cloth is judged in the process of skew detection to shield images of areas outside the cloth, the substantial characteristics and the remarkable progress are outstanding, better and more accurate detection of cloth skew data is facilitated, the generation of skew detection and weft straightening control signals of the cloth is facilitated, the production efficiency is improved, and the labor cost is reduced.
Description
Technical Field
The invention relates to the textile printing and dyeing industry, in particular to an automatic method for detecting and controlling the skewness of cloth weft on line.
Background
The cloth types are various and can be divided into knitted cloth, woven cloth, lace cloth and the like. The woven fabric and the knitted fabric can be woven into a grid, so that the grid is called as grid cloth, and the grid cloth can also be printed on the grid cloth to form printed cloth. The flower type patterns can be seen visually no matter in the plaid, lace and printed cloth, and are collectively called as flower type cloth. If no pattern is on the cloth or the cloth is a single color, the cloth is called net color cloth.
In addition to the manual detection of the skew of the weft of the piece of cloth, and the occasional manual weft straightening, the current machines carry out this task as "weft straightener". The existing weft straightener has the following disadvantages:
1. in the width range of the cloth, only the skews of a plurality of local areas are detected, and then the skews in the whole width are replaced by the skews of the local areas, so that the problem of large error exists;
2. the weft straightener mostly uses a photoelectric mode, and the problem of narrow detectable skewness range exists;
3. the automation degree of the weft straightening process is not high enough, and the requirement data of the weft skew process needs to be reset manually every time the cloth type on the weft is changed. The problems of untimely and wrong input of data input manually exist, and the quality of cloth is directly influenced.
Therefore, how to overcome the above-mentioned drawbacks and automate the whole detection and control process becomes an important issue to be solved by those skilled in the art.
Disclosure of Invention
The invention overcomes the defects of the technology and provides an automatic method for detecting and controlling the cloth skewness on line.
In order to achieve the purpose, the invention adopts the following technical scheme:
an automatic method for online detection and control of cloth skewness comprises a cloth image shooting process 1, a cloth model distinguishing process 21, a skewness detection process 22 and a weft straightening control signal generation process 3, wherein,
the cloth image shooting process 1 is specifically as follows: the control module carries out real-time shooting on the on-line cloth through the shooting module and stores data;
the cloth model distinguishing process 21 is specifically as follows: the control module judges the type of the current cloth according to the cloth image, if the type of the current cloth is judged to be clear color cloth, the characteristic data of the current clear color cloth is extracted according to the cloth image, then similarity comparison is carried out on the characteristic data of the clear color cloth in the database to obtain the type of the current clear color cloth, if the type of the current cloth is judged to be pattern cloth, the characteristic data of the current pattern cloth is extracted according to the cloth image, then similarity comparison is carried out on the characteristic data of a plurality of pattern cloths in the database to obtain the type of the current pattern cloth;
the skew detection process 22 is specifically as follows: the control module judges the width boundary of the cloth according to the cloth image so as to shield the image of the area outside the cloth, and then detects the skewing of the weft according to the image data in the width boundary of the cloth to obtain the actual skewing data of the current cloth;
the weft straightening control signal generation process 3 is specifically as follows: the control module obtains the skew requirement data corresponding to the current cloth model in the database according to the current cloth model obtained in the cloth model distinguishing process 21, then compares the skew requirement data with the actual skew data of the current cloth obtained in the skew detection process 22 to obtain an error, and calculates a weft straightening control signal so as to facilitate weft straightening control.
The automatic method for detecting and controlling the cloth weft skew on line further comprises the following steps in the cloth image shooting process 1: the control module judges the seam head between the cloth and the cloth according to the cloth image, when detecting that a new seam head arrives, the frequency of executing the cloth model distinguishing process 21 is improved, otherwise, the frequency of executing the cloth model distinguishing process 21 is reduced after a period of time.
The automatic method for detecting and controlling the cloth skewness online comprises the following specific steps of: carrying out gray processing on the cloth image, selecting a shadow pattern with the gray exceeding a preset value, judging whether the shadow pattern extends along the width direction of the cloth and the extension exceeds a preset width, if so, judging the cloth image to be a seam head, otherwise, not judging the cloth image to be the seam head.
In the automatic method for online detecting and controlling the cloth weft skew, in the cloth model distinguishing process 21, the characteristic data of the clean color cloth is CPI data and color data of the clean color cloth, and the characteristic data of the pattern cloth is pattern and/or color block spacing data of the pattern cloth.
In the automatic method for online detecting and controlling the cloth weft skew, in the cloth model distinguishing process 21, the specific process of judging whether the current cloth is clear cloth or pattern cloth is as follows: intercepting a section of strip-shaped image in the middle width area of the cloth image, wherein the long edge of the strip-shaped image is parallel to the moving direction of the cloth, setting the gray level image matrix of the strip-shaped image as A, and then solving the gray level average value B according to the rows, wherein
Wherein A [ i, j]Is a point [ i, j ] on the strip-shaped image]Then the gradient C of the column gray sum is obtained, with C [ i ]]＝B[i+t]-B[i]Where t is a natural number, t is 5-13, when any C [ i ]]If the value of (1) exceeds a certain threshold value, judging the fabric to be flower-shaped fabric, otherwise, judging the fabric to be clean-color fabric.
In the automatic method for online detection and control of cloth weft skew described above, in the weft skew detection process 22, the width boundary of the cloth is determined as follows: and carrying out gray summation of each column on the cloth image, detecting the jumping situation of the summation gray of each column in the width direction to judge the boundary position of the cloth and an external background on the image, and finally confirming the left boundary of the cloth width and the right boundary of the cloth width.
According to the automatic method for online detection and control of cloth skewness, when the skewness detection process 22 is executed, cloth actual skewness data is obtained again and again, the similarity of the cloth actual skewness data before and after detection is detected, the cloth actual skewness data detected twice is adopted when the similarity is high, and the cloth actual skewness data detected twice is ignored when the similarity is low.
Compared with the prior art, the invention has the beneficial effects that:
1. the weft straightening control signal generation process obtains the weft requirement data corresponding to the current cloth model in the database according to the current cloth model, has outstanding substantive characteristics and remarkable progress, can save the workload of manually setting the online cloth model and/or the weft requirement data, and is suitable for the condition of continuously conveying different models of cloth together by sewing; the control method has the advantages that the width boundary of the cloth is judged in the process of skew detection to shield images of areas outside the cloth, the substantial characteristics and the remarkable progress are outstanding, better and more accurate detection of cloth skew data is facilitated, the generation of skew detection and weft straightening control signals of the cloth is facilitated, the production efficiency is improved, and the labor cost is reduced.
2. The control module judges the seam head between the cloth and the cloth according to the cloth image, when detecting that a new seam head arrives, the frequency of executing the cloth model distinguishing process is improved, otherwise, the frequency of executing the cloth model distinguishing process is reduced after a period of time. Therefore, on one hand, the execution frequency of the cloth image shooting process is properly reduced to reduce the operation amount under the condition that a new seam head does not arrive and the cloth is not changed, on the other hand, the latest cloth model is detected by improving the frequency of executing the cloth model distinguishing process when the new seam head is detected, so that the weft-skew requirement data corresponding to the latest cloth model can be timely obtained in the weft-straightening control signal generating process, the error is reduced, and the method has outstanding substantive characteristics and remarkable progress.
3. And when the skewing detection process is executed, obtaining cloth actual skewing data again and again, detecting the similarity of the cloth actual skewing data of the previous and next times, acquiring and confirming the cloth actual skewing data detected twice when the similarity is higher, and ignoring the cloth actual skewing data detected twice when the similarity is lower. Therefore, the method reflects that when the same cloth is detected, the similarity of actual weft skew data of the cloth in the previous and next times is high, when the similarity is found to be low, an error detection result is obtained, and the error detection result is ignored, so that the error detection result does not participate in the subsequent control process, the elimination of the error detection result is facilitated, and the method has outstanding substantive characteristics and remarkable progress.
Drawings
Fig. 1 is a flow chart of an automatic control method of the present disclosure.
Fig. 2 is a grayscale image of cloth connected by a seam, wherein the position of a thick transverse line in the image is the seam.
Fig. 3 is a gray-scale image of a piece of cloth under a background, wherein the left and right side gray-scale abrupt change positions in the image are left and right width boundaries of the piece of cloth.
Detailed Description
The features of the present invention and other related features are further described in detail below by way of examples to facilitate understanding by those skilled in the art:
as shown in fig. 1 to 3, the automatic method for detecting and controlling the cloth skewness on line is characterized by comprising a cloth image shooting process 1, a cloth model distinguishing process 21, a skewness detecting process 22 and a weft straightening control signal generating process 3, wherein,
the cloth image shooting process 1 is specifically as follows: the control module carries out real-time shooting on the on-line cloth through the shooting module and stores data;
the cloth model distinguishing process 21 is specifically as follows: the control module judges the type of the current cloth according to the cloth image, if the type of the current cloth is judged to be clear color cloth, the characteristic data of the current clear color cloth is extracted according to the cloth image, then similarity comparison is carried out on the characteristic data of the clear color cloth in the database to obtain the type of the current clear color cloth, if the type of the current cloth is judged to be pattern cloth, the characteristic data of the current pattern cloth is extracted according to the cloth image, then similarity comparison is carried out on the characteristic data of a plurality of pattern cloths in the database to obtain the type of the current pattern cloth;
the skew detection process 22 is specifically as follows: the control module judges the width boundary of the cloth according to the cloth image so as to shield the image of the area outside the cloth, and then detects the skewing of the weft according to the image data in the width boundary of the cloth to obtain the actual skewing data of the current cloth;
the weft straightening control signal generation process 3 is specifically as follows: the control module obtains the skew requirement data corresponding to the current cloth model in the database according to the current cloth model obtained in the cloth model distinguishing process 21, then compares the skew requirement data with the actual skew data of the current cloth obtained in the skew detection process 22 to obtain an error, and calculates a weft straightening control signal so as to facilitate weft straightening control.
In the scheme, the clear color cloth and the pattern cloth are judged through the cloth model distinguishing process 21 so as to be convenient for correspondingly extracting the characteristic data of the current cloth and then are compared with the database to obtain the cloth model, the weft straightening control signal generating process 3 is convenient for obtaining the weft inclination requirement data corresponding to the current cloth model in the database according to the current cloth model, the weft straightening control signal generating process has outstanding substantive characteristics and remarkable progress, the workload of manually setting the online cloth model and/or the weft inclination requirement data can be saved, and the weft straightening control method is suitable for the situation that the cloths of different models are sewn together and continuously conveyed; in the skew detection process 22, the width boundary of the cloth is judged to shield the image of the area outside the cloth, so that the method has outstanding substantive characteristics and remarkable progress, and is beneficial to better and more accurately detecting the skew data of the cloth.
As shown in fig. 2, in a specific implementation, the cloth image capturing process 1 further includes the following steps: the control module judges the seam head between the cloth and the cloth according to the cloth image, when detecting that a new seam head arrives, the frequency of executing the cloth model distinguishing process 21 is improved, otherwise, the frequency of executing the cloth model distinguishing process 21 is reduced after a period of time. Therefore, on one hand, the execution frequency of the cloth image shooting process 1 is favorably reduced to reduce the calculation amount under the conditions that a new seam head does not arrive and the cloth is not changed, on the other hand, the latest cloth model is favorably detected by improving the frequency of executing the cloth model distinguishing process 21 when the new seam head is detected, so that the weft skew requirement data corresponding to the latest cloth model can be timely obtained in the weft straightening control signal generating process 3, the error is reduced, and the method has prominent substantive characteristics and remarkable progress.
As described above, in the specific implementation, the process of determining the seam between the pieces of cloth is as follows: carrying out gray processing on the cloth image, selecting a shadow pattern with the gray exceeding a preset value, judging whether the shadow pattern extends along the width direction of the cloth and the extension exceeds a preset width, if so, judging the cloth image to be a seam head, otherwise, not judging the cloth image to be the seam head.
As described above, in the specific implementation, in the cloth model distinguishing process 21, the characteristic data of the clean color cloth is CPI data and color data of the clean color cloth, and the characteristic data of the pattern cloth is pattern data and/or color block pitch data of the pattern cloth. In addition, the applicant declares how to extract CPI data and color data of the clean color cloth and how to extract the pattern and/or color block spacing data of the pattern cloth are not important for protection of the present case, and in the cloth model distinguishing process 21, the model of the clean color cloth and the model of the pattern cloth can also be determined by extracting other data, and a person skilled in the art can also mark the model on the cloth in advance and then determine the model of the clean color cloth and the model of the pattern cloth by detecting the model marking data on the cloth image.
As described above, in the specific implementation, in the cloth type distinguishing process 21, the specific process of determining whether the current cloth is the clear cloth or the flower-type cloth is as follows: intercepting a section of strip-shaped image in the middle width area of the cloth image, wherein the long edge of the strip-shaped image is parallel to the moving direction of the cloth, setting the gray level image matrix of the strip-shaped image as A, and then solving the gray level average value B according to the rows, wherein
Wherein A [ i, j]Is a point [ i, j ] on the strip-shaped image]Then the gradient C of the column gray sum is obtained, with C [ i ]]＝B[i+t]-B[i]Where t is a natural number, t is 5-13, when any C [ i ]]If the value of (1) exceeds a certain threshold value, judging the fabric to be flower-shaped fabric, otherwise, judging the fabric to be clean-color fabric. In addition, in the cloth model distinguishing process 21, it may be determined whether the cloth is the solid cloth or the flower-type cloth by other methods, and a person skilled in the art may also perform model marking on the cloth in advance, and then determine whether the cloth is the solid cloth or the flower-type cloth by detecting the model marking on the cloth image.
As shown in fig. 3, in the skew detection process 22, the width boundary of the cloth is determined as follows: and carrying out gray summation of each column on the cloth image, then judging the boundary position of the cloth and an external background on the image by detecting the jumping condition of the summed gray in the width direction, and finally confirming the left boundary of the cloth width and the right boundary of the cloth width.
As described above, in a specific implementation, when executing the skew detection process 22, the cloth actual skew data is obtained again and again, the similarity of the cloth actual skew data of the previous and subsequent times is detected, the cloth actual skew data detected twice is adopted when the similarity is higher, and the cloth actual skew data detected twice is ignored when the similarity is lower. Therefore, the method reflects that when the same cloth is detected, the similarity of actual weft skew data of the cloth in the previous and next times is high, when the similarity is found to be low, an error detection result is obtained, and the error detection result is ignored, so that the error detection result does not participate in the subsequent control process, the elimination of the error detection result is facilitated, and the method has outstanding substantive characteristics and remarkable progress.
As described above, how to obtain the actual skewing data of the cloth from the cloth image is not a protection focus of the present application, and therefore, a more specific process is not described in detail.
As described above, the present application protects an automatic method for detecting and controlling the cloth weft skew on line, and all technical solutions that are the same as or similar to the present application should be considered to fall within the protection scope of the present application.
Claims (6)
1. An automatic method for online detection and control of cloth skewness is characterized by comprising a cloth image shooting process (1), a cloth model distinguishing process (21), a skewness detection process (22) and a weft straightening control signal generation process (3), wherein,
the cloth image shooting process (1) is as follows: the control module carries out real-time shooting on the on-line cloth through the shooting module and stores data;
the cloth model distinguishing process (21) is specifically as follows: the control module judges the type of the current cloth according to the cloth image, if the type of the current cloth is judged to be clear color cloth, the characteristic data of the current clear color cloth is extracted according to the cloth image, then similarity comparison is carried out on the characteristic data of the clear color cloth in the database to obtain the type of the current clear color cloth, if the type of the current cloth is judged to be pattern cloth, the characteristic data of the current pattern cloth is extracted according to the cloth image, then similarity comparison is carried out on the characteristic data of a plurality of pattern cloths in the database to obtain the type of the current pattern cloth;
the skew detection process (22) is specifically as follows: the control module judges the width boundary of the cloth according to the cloth image so as to shield the image of the area outside the cloth, and then detects the skewing of the weft according to the image data in the width boundary of the cloth to obtain the actual skewing data of the current cloth;
the weft straightening control signal generation process (3) is concretely as follows: and the control module acquires the weft skew requirement data corresponding to the current cloth model in the database according to the current cloth model acquired in the cloth model distinguishing process (21), compares the weft skew requirement data with the actual weft skew data of the current cloth acquired in the weft skew detection process (22), acquires an error, and calculates a weft straightening control signal so as to facilitate weft straightening control.
2. The automatic method for the online detection and control of the cloth weft skew according to claim 1, characterized in that the cloth image shooting process (1) further comprises the following steps: the control module judges the seam head between the cloth and the cloth according to the cloth image, when detecting that a new seam head arrives, the frequency of executing the cloth model distinguishing process (21) is improved, otherwise, the frequency of executing the cloth model distinguishing process (21) is reduced after a period of time.
3. The automatic method for the online detection and control of the cloth weft skew according to claim 2, characterized in that the process of judging the seam between the cloth and the cloth is as follows: carrying out gray processing on the cloth image, selecting a shadow pattern with the gray exceeding a preset value, judging whether the shadow pattern extends along the width direction of the cloth and the extension exceeds a preset width, if so, judging the cloth image to be a seam head, otherwise, not judging the cloth image to be the seam head.
4. The automatic method for online detection and control of cloth weft skew according to claim 1, characterized in that in the cloth model distinguishing process (21), the characteristic data of the clean cloth is CPI data and color data of the clean cloth, and the characteristic data of the pattern cloth is pattern and/or color block spacing data of the pattern cloth.
5. The automatic method for online detection and control of cloth weft skew according to claim 1, characterized in that in the weft skew detection process (22), the width boundary of the cloth is determined as follows: and carrying out gray summation of each column on the cloth image, detecting the jumping situation of the summation gray of each column in the width direction to judge the boundary position of the cloth and an external background on the image, and finally confirming the left boundary of the cloth width and the right boundary of the cloth width.
6. The automatic method for the online detection and control of the cloth skews according to any one of claims 1-5, characterized in that when executing the skewing detection process (22), the cloth skewing data is obtained again and again, the similarity of the cloth skewing data before and after detection is detected, the cloth skewing data detected twice is collected when the similarity is higher, and the cloth skewing data detected twice is ignored when the similarity is lower.
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