Visual detection system based on fabric fiber center line judgment and detection method thereof

Abstract

The invention is suitable for the technical field of fabric detection, and provides a visual detection system based on fabric fiber center line judgment and a detection method thereof.The visual detection system comprises a visual identification unit composed of a plurality of visual identification modules, the visual identification modules are used for irradiating and detecting a plurality of filaments on the surface of a fabric, each group of filaments comprises y filaments, the surface of the whole fabric comprises x groups of filaments; the illumination detection unit is used for uniformly irradiating the surface of the fabric to ensure that each filament can be clearly identified; wherein the illumination detection unit comprises an illumination source and a detection device, the illumination source adopts a combination of a plurality of light sources, and the device solves the problem that in the fabric production process, once it is detected that filaments deviate or break, the tension or position of the fabric cannot be effectively adjusted so as to ensure alignment of the filaments and a virtual center line and uniformity of the fabric. The purpose that each filament is accurately aligned with the virtual center line, and then the more accurate cutting process is achieved is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of fabric detection, and more particularly to a visual detection system and a detection method thereof based on fabric fiber centerline determination. Background Art

[0002] Fabric inspection technology is widely used in the textile industry to detect the quality, neatness and consistency of fabrics. With the development of automation in the textile production process, traditional manual inspection can no longer meet the requirements of efficiency and accuracy. Modern fabric inspection systems use image processing technology to shoot fabrics with high-resolution cameras, analyze images using algorithms, identify filaments, stitches and other features on the fabric surface, and promptly detect possible defects in the fabric, such as breakage, dislocation or uneven spacing. These technologies are of great significance in improving production efficiency and product quality.

[0003] Existing fabric detection technologies mostly rely on simple image analysis methods and have not yet been able to effectively handle the precise positioning and alignment of filaments in fabrics. Especially when the filaments are dense or the fabric surface is complex, the detection accuracy is low and is easily affected by external lighting changes and fabric deformation. At the same time, existing technologies often fail to comprehensively consider the overall tension adjustment of the fabric and the real-time correction of the filament spacing, resulting in the inability to effectively adjust the tension or position of the fabric during the fabric production process once the filament offset or breakage is detected to ensure the alignment of the filaments with the virtual center line and the uniformity of the fabric. Therefore, in order to accurately cut the fabric, it is still necessary to solve the related problems of filament position, deviation correction and overall tension control of the fabric to ensure that each filament is accurately aligned with the virtual center line, thereby achieving a more precise cutting process.

[0004] Therefore, the present invention proposes a visual inspection system and a inspection method based on fabric fiber centerline determination. Summary of the invention

[0005] In view of the deficiencies in the prior art, the object of the present invention is to provide a visual inspection system and a method thereof based on fabric fiber centerline determination.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The visual inspection system based on the determination of the center line of fabric fibers includes:

[0008] A visual recognition unit, composed of a plurality of visual recognition modules, wherein the visual recognition modules are used to illuminate and detect a number of filaments on the surface of the fabric, each group of filaments contains y filaments, and the entire fabric surface contains x groups of filaments;

[0009] A light detection unit, which is used to evenly illuminate the surface of the fabric to ensure that each filament can be clearly identified;

[0010] The light detection unit includes a light source and a detection device. The light source is a combination of multiple light sources, which can control the irradiation angle and intensity between the light source and the fabric to avoid the image quality being affected by light spots or shadows. The detection device includes a high-resolution camera for capturing images of the fabric surface.

[0011] An image processing unit, the image processing unit is used to process the captured image, identify the brightest points in the filament through a gray value algorithm, and connect these bright points into lines to obtain the accurate position of the filament;

[0012] A centerline deviation judgment unit, the centerline deviation judgment unit is used to calculate the deviation between the position of each filament and the virtual centerline, and if the deviation exceeds a predetermined threshold, it is judged that the centerline position is not aligned;

[0013] The control drive module drives the cloth pulling mechanism to make adjustments when filament deviation, inconsistent spacing or filament breakage is detected to ensure that the center line position is aligned with the virtual center line.

[0014] The present invention is further configured as follows: the visual recognition unit comprises x groups of visual recognition modules, each group of visual recognition modules illuminates and detects y filaments on the fabric, and the detection of each group of filaments is performed independently.

[0015] By adopting the above technical solution, the efficiency and accuracy of the detection process are ensured through independent detection, and the overlap, occlusion or interference of multiple filaments that may occur during the detection process is avoided. By independently detecting each group of filaments, the system can analyze the quality of the filaments one by one, thereby realizing accurate monitoring of the filaments on the surface of the fabric. Each visual recognition module only focuses on the group of filaments it illuminates when working, and the visual signals between different groups will not cross or interfere, so that the system shows good stability and reliability in the detection of high-density fabrics.

[0016] The present invention is further configured as follows: the image processing unit selects the brightest point in the filament through a gray value algorithm, and connects these bright points into a line to obtain the accurate position of the filament.

[0017] The present invention is further configured as follows: the center line deviation judgment unit calculates the deviation value between each filament and the virtual center line to judge whether a deviation occurs, and when the deviation exceeds a set threshold, it is judged as a filament deviation.

[0018] The present invention is further configured such that: the control drive module adjusts the cloth pulling mechanism according to the offset judgment result so that the center line of the filament is aligned with the virtual center line.

[0019] The present invention is further configured such that: the image processing unit can automatically detect the problem of filament breakage and inconsistent spacing, and when some filaments in the image are lost or deviate from the set position, the control drive module drives the cloth pulling mechanism to make adjustments.

[0020] The detection method based on fabric fiber centerline determination, according to the above-mentioned visual detection system based on fabric fiber centerline determination, comprises the following steps:

[0021] S1. The light source evenly illuminates the fabric surface to ensure that each filament can be clearly identified, and the image of the fabric surface is collected by a high-resolution camera or video camera;

[0022] S2, collecting images through a visual recognition unit;

[0023] S3, the image processing unit processes the collected image to obtain a processed image;

[0024] S4, calculating the deviation between the filament and the virtual center line according to the filament position obtained in step S3, and calculating the deviation between each filament and the preset virtual center line by the center line deviation judgment unit, and if the deviation value exceeds a predetermined allowable range, it is determined that the filament is offset, broken or the spacing is inconsistent;

[0025] S5. According to the deviation result of step S4, the driving module is controlled to adjust the cloth pulling mechanism, and the cloth pulling mechanism is driven to perform fine adjustment.

[0026] The present invention is further configured as follows: the specific steps of S3 are:

[0027] S31, grayscale processing: convert the collected color image into a grayscale image to obtain the grayscale value G'(i, j) of each pixel, where i and j represent the position of each pixel in the image, and the grayscale value G'(i, j) is calculated by the following custom weighting functions α and β:

[0028] G'(i,j)=α·C1(i,j)+β·C2(i,j)+(1-α-β)·C3(i,j)

[0029] Where C1(i,j), C2(i,j), and C3(i,j) are the red, green, and blue pixel values ​​at position (i,j) in the image, respectively. α and β are weight coefficients, and α+β≤1. Custom weights are helpful to adapt to different types of image data.

[0030] S32, threshold processing: set a threshold T' as a judgment standard, which is used to convert the grayscale image into a binary image. For each pixel (i, j), if the grayscale value G'(i, j) is greater than the threshold T', the pixel is set to white (value is 1); otherwise, it is set to black (value is 0);

[0031] The formula for this process is:

[0032]

[0033] Where B′(i, j) is the pixel value of the binarized image, and T′ is a custom threshold value that is dynamically adjusted according to different images or application scenarios;

[0034] S33, binarization processing: After the binary image is generated by the above binarization operation, the image processing unit further analyzes the shape of the filaments, connects the filaments into continuous line segments through the binary image, and performs further precision analysis and identification to calculate the filament position deviation and detect its direction, alignment, spacing consistency and possible breakage.

[0035] The present invention is further configured as follows: S5 comprises the following steps:

[0036] S51. Calculate the deviation between each filament and the virtual center line:

[0037] Δp(i)=pactual(i)-pvirtual

[0038] Where Δp(i) is the deviation between the actual position of the ith filament and the virtual centerline, pactual(i) is the actual position of the ith filament, and pvirtual is the position of the virtual centerline;

[0039] S52. Calculate the corrected position of each filament according to the deviation value:

[0040] padjusted(i)=pactual(i)-k·Δp(i)

[0041] Among them, padjusted(i) is the new position of the i-th filament after adjustment, and k is the adjustment coefficient, which controls the magnitude of the adjustment;

[0042] S53, calculating the fabric tension adjustment amount according to the deviation of the filaments to ensure fabric position adjustment:

[0043] ΔT(i)=k′·|Δp(i)|

[0044] Where ΔT(i) is the tension adjustment of the i-th filament, k′ is the tension adjustment coefficient, and |Δp(i)| is the absolute value of the filament deviation;

[0045] S54, controlling the cloth pulling mechanism to adjust the tension and position of the fabric according to the calculated adjustment amount ΔT(i) and padjusted(i) to ensure that the filaments are aligned with the virtual center line to meet the following conditions:

[0046] padjusted(i)≈pvirtual

[0047] Among them, padjusted(i) is the position of the filament after adjustment, and the final filament position should be close to the virtual midline position pvirtual.

[0048] The present invention is further configured as follows: the cloth pulling mechanism adjusts the tension and position of the fabric so that the filaments on the fabric are aligned with the virtual center line to ensure that the fabric is neat; if the position of the filaments is not completely aligned, the system will gradually adjust the cloth pulling mechanism according to the deviation value and make repeated corrections until the center line of the filaments is aligned with the virtual center line;

[0049] This process also includes real-time detection of the spacing between filaments. If the spacing is found to be inconsistent, the drive system adjusts the tension between the filaments to ensure the uniformity of the fabric.

[0050] In summary, the present application includes at least one of the following beneficial technical effects:

[0051] 1. This application detects fabric filaments independently, which ensures the efficiency and accuracy of the detection process and avoids the overlap, occlusion or interference of multiple filaments during the detection process. By independently detecting each group of filaments, the system can analyze the quality of the filaments one by one, thereby achieving accurate monitoring of the filaments on the surface of the fabric. Each visual recognition module only focuses on the group of filaments it illuminates when working, and the visual signals between different groups will not cross or interfere, so that the system shows good stability and reliability in the detection of high-density fabrics.

[0052] 2. The threshold setting in this application is flexible, allowing operators to customize it according to different application requirements. Operators can manually adjust the threshold size according to specific detection accuracy requirements. For example, in applications with high precision requirements, the threshold can be set smaller to accurately identify even small deviations; while in large-scale, rougher fabric detection, the threshold can be appropriately relaxed to accommodate a greater error tolerance. Through this flexible threshold adjustment method, the system can balance accuracy and efficiency in different production environments and demand scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a module schematic diagram of the visual inspection system based on fabric fiber centerline determination of the present invention. DETAILED DESCRIPTION

[0054] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0055] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as commonly understood by ordinary technicians in the technical field to which this application belongs.

[0056] See also Figure 1 , the present invention provides the following technical solutions:

[0057] For example, see Figure 1 , a visual inspection system based on fabric fiber centerline determination, including:

[0058] The visual recognition unit is composed of a plurality of visual recognition modules, and the visual recognition modules are used to illuminate and detect a plurality of filaments on the surface of the fabric, each group of filaments contains y filaments, and the entire fabric surface contains x groups of filaments.

[0059] It should be noted that each group of visual recognition modules only detects y filaments independently, and the detection processes of these filaments will not interfere with each other. Each module can accurately capture the image data of the group of filaments without being affected by other groups, and analyze these data through the image processing unit to obtain the position, direction and possible deviation of the filaments.

[0060] Independent detection ensures the efficiency and accuracy of the detection process, and avoids the overlap, occlusion or interference of multiple filaments during the detection process. By independently detecting each group of filaments, the system can analyze the quality of the filaments one by one, thereby achieving accurate monitoring of the filaments on the fabric surface. Each visual recognition module only focuses on the filament group it illuminates when working, and the visual signals between different groups will not cross or interfere, making the system show good stability and reliability in the detection of high-density fabrics.

[0061] Light detection unit, which is used to evenly illuminate the fabric surface to ensure that each filament can be clearly identified;

[0062] The light detection unit includes a light source and a detection device. The light source adopts a combination of multiple light sources, which can control the irradiation angle and intensity between the light source and the fabric to avoid the image quality being affected by light spots or shadows. The detection device includes a high-resolution camera for capturing images of the fabric surface.

[0063] The image processing unit is used to process the captured image, identify the brightest point in the filament through the gray value algorithm, and connect these bright points into a line to obtain the accurate position of the filament; specifically, the image processing unit selects the brightest point in the filament through the gray value algorithm, and connects these bright points into a line to obtain the accurate position of the filament. The image processing unit can automatically detect the problem of filament breakage and inconsistent spacing. When some filaments in the image are missing or deviate from the set position, the control drive module drives the cloth pulling mechanism to make adjustments.

[0064] The centerline offset judgment unit is used to calculate the deviation between the position of each filament and the virtual centerline. If the deviation exceeds the predetermined threshold, it is judged that the centerline position is not aligned; the centerline offset judgment unit calculates the deviation value between each filament and the virtual centerline to determine whether an offset occurs. When the deviation exceeds the set threshold, it is judged as a filament offset. Among them, the setting of the threshold is flexible, allowing operators to customize it according to different application requirements. The operator can manually adjust the size of the threshold according to the specific detection accuracy requirements. For example, in applications with high precision requirements, the threshold can be set smaller, so as to accurately identify even small deviations; and in large-scale, relatively rough fabric detection, the threshold can be appropriately relaxed to accommodate a greater error tolerance. Through this flexible threshold adjustment method, the system can balance accuracy and efficiency in different production environments and demand scenarios.

[0065] The control drive module drives the cloth pulling mechanism to make adjustments when filament offset, inconsistent spacing or filament breakage is detected to ensure that the centerline position is aligned with the virtual centerline. In the case of filament offset, the control drive module first adjusts the tension and position of the fabric through the cloth pulling mechanism, and gradually adjusts the position of the filaments to ensure that the filaments are realigned with the virtual centerline. When the spacing between filaments is inconsistent, the system calculates the tension adjustment amount for each filament, and drives the cloth pulling mechanism to accurately adjust the spacing between the filaments based on these adjustments to make it more uniform. In the case of filament breakage, the control drive module detects the break position in the image and attempts to restore or rearrange the broken filaments by adjusting the stretching force of the fabric to ensure that the overall structure of the fabric is not affected.

[0066] Embodiment 2, a detection method based on fabric fiber centerline determination, according to the above-mentioned visual detection system based on fabric fiber centerline determination, comprises the following steps:

[0067] S1. The light source evenly illuminates the fabric surface to ensure that each filament can be clearly identified, and the image of the fabric surface is collected by a high-resolution camera or video camera;

[0068] S2, collecting images through a visual recognition unit;

[0069] S3, the image processing unit processes the collected image to obtain a processed image;

[0070] S4, calculating the deviation between the filament and the virtual center line according to the filament position obtained in step S3, and calculating the deviation between each filament and the preset virtual center line by the center line deviation judgment unit, and if the deviation value exceeds a predetermined allowable range, it is determined that the filament is offset, broken or the spacing is inconsistent;

[0071] S5, according to the deviation result of step S4, controlling the driving module to adjust the cloth pulling mechanism, and driving the cloth pulling mechanism to perform fine adjustment;

[0072] The specific steps of S3 are:

[0073] S31, grayscale processing: convert the collected color image into a grayscale image to obtain the grayscale value G'(i, j) of each pixel, where i and j represent the position of each pixel in the image, and the grayscale value G'(i, j) is calculated by the following custom weighting functions α and β:

[0074] G'(i,j)=α·C1(i,j)+β·C2(i,j)+(1-α-β)·C3(i,j)

[0075] Where C1(i,j), C2(i,j), and C3(i,j) are the red, green, and blue pixel values ​​at position (i,j) in the image, respectively. α and β are weight coefficients, and α+β≤1. Custom weights are helpful to adapt to different types of image data.

[0076] S32, threshold processing: set a threshold T' as the judgment standard to convert the grayscale image into a binary image. For each pixel (i, j), if the grayscale value G'(i, j) is greater than the threshold T', the pixel is set to white (value 1); otherwise, it is set to black (value 0);

[0077] The formula for this process is:

[0078]

[0079] Among them, B′(i, j) is the pixel value of the binarized image, and T′ is a custom threshold value that is dynamically adjusted according to different images or application scenarios.

[0080] S33, binarization processing: After the binary image is generated by the above binarization operation, the image processing unit further analyzes the shape of the filaments, connects the filaments into continuous line segments through the binary image, and performs further precision analysis and identification to calculate the filament position deviation and detect its direction, alignment, spacing consistency and possible breakage.

[0081] S5 includes the following steps:

[0082] S51. Calculate the deviation between each filament and the virtual center line:

[0083] Δp(i)=pactual(i)-pvirtual

[0084] Where Δp(i) is the deviation between the actual position of the ith filament and the virtual centerline, pactual(i) is the actual position of the ith filament, and pvirtual is the position of the virtual centerline;

[0085] S52. Calculate the corrected position of each filament according to the deviation value:

[0086] padjusted(i)=pactual(i)-k·Δp(i)

[0087] Among them, padjusted(i) is the new position of the i-th filament after adjustment, and k is the adjustment coefficient, which controls the magnitude of the adjustment;

[0088] S53, calculating the fabric tension adjustment amount according to the deviation of the filaments to ensure fabric position adjustment:

[0089] ΔT(i)=k′·|Δp(i)|

[0090] Where ΔT(i) is the tension adjustment of the i-th filament, k′ is the tension adjustment coefficient, and |Δp(i)| is the absolute value of the filament deviation;

[0091] S54, controlling the cloth pulling mechanism to adjust the tension and position of the fabric according to the calculated adjustment amount ΔT(i) and padjusted(i) to ensure that the filaments are aligned with the virtual center line to meet the following conditions:

[0092] padjusted(i)≈pvirtual

[0093] Among them, padjusted(i) is the position of the filament after adjustment, and the final filament position should be close to the virtual midline position pvirtual.

[0094] The cloth pulling mechanism adjusts the tension and position of the fabric to align the filaments on the fabric with the virtual center line, ensuring the neatness of the fabric; if the position of the filaments is not completely aligned, the system will gradually adjust the cloth pulling mechanism according to the deviation value, and make repeated corrections until the center line of the filaments is aligned with the virtual center line.

[0095] This process also includes real-time detection of the spacing between filaments. If the spacing is found to be inconsistent, the drive system adjusts the tension between the filaments to ensure the uniformity of the fabric.

[0096] The device embodiments described above are merely illustrative and are not all embodiments. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms. All other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

Claims

1. A visual inspection system based on fabric fiber centerline determination, characterized by: include: A visual recognition unit, composed of a plurality of visual recognition modules, wherein the visual recognition modules are used to illuminate and detect a number of filaments on the surface of the fabric, each group of filaments contains y filaments, and the entire fabric surface contains x groups of filaments; A light detection unit, which is used to evenly illuminate the surface of the fabric to ensure that each filament can be clearly identified; The light detection unit includes a light source and a detection device. The light source is a combination of multiple light sources, which can control the irradiation angle and intensity between the light source and the fabric to avoid the image quality being affected by light spots or shadows. The detection device includes a high-resolution camera for capturing images of the fabric surface. An image processing unit, the image processing unit is used to process the captured image, identify the brightest points in the filament through a gray value algorithm, and connect these bright points into lines to obtain the accurate position of the filament; A centerline deviation judgment unit, the centerline deviation judgment unit is used to calculate the deviation between the position of each filament and the virtual centerline, and if the deviation exceeds a predetermined threshold, it is judged that the centerline position is not aligned; The control drive module drives the cloth pulling mechanism to make adjustments when filament deviation, inconsistent spacing or filament breakage is detected to ensure that the center line position is aligned with the virtual center line.

2. The visual inspection system based on fabric fiber centerline determination according to claim 1, characterized in that: The visual recognition unit comprises x groups of visual recognition modules, each group of visual recognition modules illuminates and detects y filaments on the fabric, and the detection of each group of filaments is performed independently.

3. The visual inspection system based on fabric fiber centerline determination according to claim 1, characterized in that: The image processing unit selects the brightest point in the filament through a gray value algorithm, and connects these bright points into a line to obtain the accurate position of the filament.

4. The visual inspection system based on fabric fiber centerline determination according to claim 1, characterized in that: The center line deviation judgment unit calculates the deviation value between each filament and the virtual center line to judge whether a deviation occurs. When the deviation exceeds a set threshold, it is judged as a filament deviation.

5. The visual inspection system based on fabric fiber centerline determination according to claim 1, characterized in that: The control driving module adjusts the cloth pulling mechanism according to the offset judgment result to align the center line of the filament with the virtual center line.

6. The visual inspection system based on fabric fiber centerline determination according to claim 1, characterized in that: The image processing unit can automatically detect the problem of filament breakage and inconsistent spacing. When some filaments in the image are lost or deviate from the set position, the control drive module drives the cloth pulling mechanism to make adjustments.

7. A detection method based on fabric fiber centerline determination, using a visual detection system based on fabric fiber centerline determination as claimed in any one of claims 1 to 6, characterized in that: The following steps are involved: S1. The light source evenly illuminates the fabric surface to ensure that each filament can be clearly identified, and the image of the fabric surface is collected by a high-resolution camera or video camera; S2, collecting images through a visual recognition unit; S3, the image processing unit processes the collected image to obtain a processed image; S4, calculating the deviation between the filament and the virtual center line according to the filament position obtained in step S3, and calculating the deviation between each filament and the preset virtual center line by the center line deviation judgment unit, and if the deviation value exceeds a predetermined allowable range, it is determined that the filament is offset, broken or the spacing is inconsistent; S5. According to the deviation result of step S4, the driving module is controlled to adjust the cloth pulling mechanism, and the cloth pulling mechanism is driven to perform fine adjustment.

8. The detection method based on fabric fiber centerline determination according to claim 7 is characterized in that: The specific steps of S3 are: S31, grayscale processing: convert the collected color image into a grayscale image to obtain the grayscale value G'(i, j) of each pixel, where i and j represent the position of each pixel in the image, and the grayscale value G'(i, j) is calculated by the following custom weighting functions α and β: G'(i,j)=α·C1(i,j)+β·C2(i,j)+(1-α-β)·C3(i,j) Where C1(i,j), C2(i,j), and C3(i,j) are the red, green, and blue pixel values ​​at position (i,j) in the image, respectively. α and β are weight coefficients, and α+β≤1. Custom weights are helpful to adapt to different types of image data. S32, threshold processing: set a threshold T' as a judgment standard, which is used to convert the grayscale image into a binary image. For each pixel (i, j), if the grayscale value G'(i, j) is greater than the threshold T', the pixel is set to white (value is 1); otherwise, it is set to black (value is 0); The formula for this process is: Where B'(i,j) is the pixel value of the binarized image, and T' is a custom threshold value that is dynamically adjusted according to different images or application scenarios; S33, binarization processing: After the binary image is generated by the above binarization operation, the image processing unit further analyzes the shape of the filaments, connects the filaments into continuous line segments through the binary image, and performs further precision analysis and identification to calculate the filament position deviation and detect its direction, alignment, spacing consistency and possible breakage.

9. The detection method based on fabric fiber centerline determination according to claim 7, characterized in that: The S5 comprises the following steps: S51. Calculate the deviation between each filament and the virtual center line: Δp(i)=pactual(i)-pvirtual Where Δp(i) is the deviation between the actual position of the ith filament and the virtual centerline, pactual(i) is the actual position of the ith filament, and pvirtual is the position of the virtual centerline; S52. Calculate the corrected position of each filament according to the deviation value: padjusted(i)=pactual(i)-k·Δp(i) Among them, padjusted(i) is the new position of the i-th filament after adjustment, and k is the adjustment coefficient, which controls the magnitude of the adjustment; S53, calculating the fabric tension adjustment amount according to the deviation of the filaments to ensure fabric position adjustment: ΔT(i)=k′·|Δp(i)| Among them, ΔT(i) is the tension adjustment of the i-th filament, k′ is the tension adjustment coefficient, and |Δp(i)| is the absolute value of the filament deviation; S54, controlling the cloth pulling mechanism to adjust the tension and position of the fabric according to the calculated adjustment amount ΔT(i) and padjusted(i) to ensure that the filaments are aligned with the virtual center line to meet the following conditions: padjusted(i)≈pvirtual Among them, padjusted(i) is the position of the filament after adjustment, and the final filament position should be close to the virtual midline position pvirtual.

10. The detection method based on fabric fiber centerline determination according to claim 7, characterized in that: The cloth pulling mechanism adjusts the tension and position of the fabric to align the filaments on the fabric with the virtual center line to ensure the neatness of the fabric; if the position of the filaments is not completely aligned, the system will gradually adjust the cloth pulling mechanism according to the deviation value and make repeated corrections until the center line of the filaments is aligned with the virtual center line; This process also includes real-time detection of the spacing between filaments. If the spacing is found to be inconsistent, the drive system adjusts the tension between the filaments to ensure the uniformity of the fabric.

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