CN118247231A - Spinning quality visual identification system - Google Patents

Abstract

The present invention relates to the field of visual recognition, and specifically discloses a spinning quality visual recognition system, comprising a processor, a spinning product image acquisition module and a spinning quality visual recognition module which are communicatively connected to the processor, and are used to solve the problem that the convexity and concavity of a local shadow area cannot be determined when the spinning product image is recognized; the present invention collects spinning product images through a high-resolution camera, performs grayscale threshold segmentation on the spinning product images, divides the spinning product images into a plurality of image area blocks, removes non-shadow areas, obtains shadow areas in the spinning product images, distinguishes shadow areas as convex areas or concave areas, and evaluates the quality of the spinning products by constructing a spinning product quality evaluation model, which can effectively solve the problem that the convexity and concavity of a local shadow area cannot be determined when the spinning product image is recognized, and helps to improve the accuracy and reliability of the evaluation of the quality of the spinning products.

Description

A visual recognition system for spinning quality

Technical Field

The present invention relates to the field of visual recognition, and more particularly to a visual recognition system for spinning quality.

Background technique

The traditional detection method for the quality inspection of spinning products often uses the eyes of operators to identify defects, but this detection method has the problems of high missed detection rate, high work intensity and being greatly affected by the subjective factors of operators. These problems will lead to a decline in the quality of textiles, thereby affecting the tactile comfort of customers. Applying visual recognition to the defect identification of spinning products improves the accuracy and efficiency of defect identification and enhances the ability to distinguish the types of defects. At present, when visually identifying spinning products, it is found that some decorative areas of spinning products with decorations often need to be uniformly designed with convex parts, but due to the mechanical friction during the weaving process or the uneven arrangement of fibers, the spinning products have different degrees of concavity and convexity, which makes the clarity of the image defective. When comparing the shadow areas with more shadows in the image, it is impossible to determine the concavity and convexity of the local shadow areas, which affects the comfort of customers and affects the aesthetics of the image and appearance of the spinning products. In order to solve the above problems, a technical solution is provided.

Summary of the invention

In order to overcome the above-mentioned defects of the prior art, the present invention provides a spinning quality visual recognition system, which collects spinning product images through a high-resolution camera, divides the spinning product images into several image area blocks through grayscale threshold segmentation, and eliminates the non-shadow area to obtain the shadow area in the spinning product image, and distinguishes the shadow area as a convex area or a concave area. The quality of the spinning product is evaluated by constructing a spinning product quality evaluation model, which can effectively solve the problem that the convexity and concaveness of the local shadow area cannot be determined when identifying the spinning product image, and helps to improve the accuracy and reliability of the evaluation of the spinning quality, so as to solve the problems raised in the above-mentioned background technology.

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

A spinning quality visual recognition system comprises a processor, a spinning product image acquisition module and a spinning quality visual recognition module which are communicatively connected to the processor, wherein the spinning product image acquisition module is used to collect spinning product images through a high-resolution camera; the spinning quality visual recognition module is used to visually recognize the spinning product images and perform quality assessment, the spinning quality visual recognition module comprises an image processing unit, a shadow area segmentation unit, an area detection unit and a spinning quality assessment unit, the spinning quality assessment unit is used to assess the quality of the spinning product by constructing a spinning product quality assessment model, and assess the quality of the spinning product by acquiring the pore size abnormality value of the spinning product, the total area of the spinning product image of the concave area defect value and the total area of G pore image blocks in the concave area, and importing the pore size abnormality value of the spinning product, the total area of the spinning product image of the concave area defect value and the total area of G pore image blocks in the concave area into the spinning product quality assessment model, wherein the formula of the spinning product quality assessment model is:

Where: zl _f is the quality index of the spinning product, aq _x is the defect value of the concave area, yf _x is the pore size abnormality value of the spinning product, S _z is the total area of the spinning product image with defect value in the concave area, and S _g is the total area of G pore image blocks in the concave area.

As a further solution of the present invention, the image processing unit is used to remove noise, enhance contrast and grayscale the spinning product image;

The shadow area segmentation unit is used to segment the spinning product image into a plurality of image area blocks through grayscale threshold segmentation, and remove the non-shadow area to obtain the shadow area in the spinning product image;

The region detection unit is used to distinguish whether the shadow region is a convex region or a concave region;

The spinning quality evaluation unit is used to evaluate the quality of spinning products by constructing a spinning product quality evaluation model.

As a further solution of the present invention, the shadow area segmentation unit includes a non-shadow area identification subunit, a non-shadow area segmentation subunit and a shadow area extraction subunit, wherein:

The non-shadow area recognition unit is used to recognize the non-shadow area in the spinning product image;

The non-shadow region segmentation subunit is used for segmenting the non-shadow region in the spinning product image;

The shadow area extraction unit is used to extract the remaining image area in the spinning product image, and the remaining image area is the shadow area.

As a further solution of the present invention, the non-shadow area segmentation subunit is used to segment the non-shadow area in the spinning product image, divide the spinning product image into a number of pore image blocks according to the specified pore size of spinning, eliminate the pore image blocks in the non-shadow area according to the spinning target grayscale value, and leave the pore image blocks in the shadow area.

As a further solution of the present invention, the spinning target grayscale value is obtained by calculating the average grayscale range of each pore image block, and the grayscale range of the pore image blocks is arranged in descending order as a range sequence {K _h (i _h ,j _h ),…,K ₂ (i ₂ ,j ₂ ),K ₁ (i ₁ ,j ₁ )}, wherein K ₁ (i ₁ ,j ₁ ) is the pore image block with the smallest grayscale range value, K 2 (i ₂ ,j ₂ ) is the pore image block with the second smallest grayscale range value, and K _h (i _h ,j _h ) is the pore image block with the largest grayscale range value; the 40% range sequence of grayscale range from large to small {K 0.4h (i _0.4h ,j 0.4h ),…,K ₂ (i 2 ,j 2 ),K ₁ (i 1 ,j 1 )} is arranged in descending order as a range sequence {K _0.4h (i 0.4h ,j _0.4h ),…,K ₂ (i ₂ ,j ₂ ),K 1 (i ₁ ,j _{1 )} } )}, where K _0.4h (i _0.4h ,j _0.4h ) is the pore image block whose grayscale range value is 40% of the maximum value; the grayscale range value of the spinning target grayscale value of the remaining range sequence is compared with the spinning target grayscale value, and the pore image blocks whose grayscale range value is greater than the spinning target grayscale value are taken as shadow areas and marked.

As a further solution of the present invention, the region detection unit includes a concave-convex region discrimination unit, a convex region output unit and a concave region output unit; the concave-convex region discrimination unit is used to distinguish the concave-convexity of the shadow region by calculating the grayscale mean of the pore image block in the shadow region, comparing the grayscale value of the pore image block with the grayscale mean, and comparing the grayscale value of the pore image block with the grayscale mean in the following manner:

when When , the pore image block is a convex area;

when When , the pore image block is a concave area;

Where s _Hq is the gray value of the qth pore image block, is the grayscale mean of the pore image block.

As a further solution of the present invention, the spinning quality evaluation unit includes a pore size anomaly analysis unit, a convex area statistical unit and a quality evaluation unit. The pore size anomaly analysis unit is used to extract the pore area in each pore image block in the spinning product image, arrange the pore area of each pore image block from large to small according to the numerical value, and calculate the average pore area of each pore image block. The pore area of each pore image block and the average pore area of each pore image block are introduced into the pore size anomaly analysis calculation model to calculate the pore size anomaly value of the spinning product, wherein the calculation formula of the anomaly analysis calculation model is:

Where: yf _x is the abnormal value of the pore size of the spinning product, is the mean pore area of each pore image block, P is the total number of pore image blocks, kx _pmax is the maximum pore area in pore image block p, and kx _pmin is the minimum pore area in pore image block p.

As a further solution of the present invention, the convex area statistical unit is used to count the pore image blocks in the concave area, obtain the number of pore image blocks contained in the concave area, the area of each pore image block and the average area of the pore image block, and import the number of pore image blocks in the concave area, the area of each pore image block and the average area of the pore image block into the concave area defect value calculation formula to calculate the concave area defect value in the spinning product, wherein the concave area defect value calculation formula is:

Where: _aqx is the defect value of the concave area, _Sg is the area of the g-th pore image block in the concave area, G is the total number of pore image blocks in the concave area, is the average area of the pore image blocks in the concave area.

The technical effects and advantages of a spinning quality visual recognition system of the present invention are as follows: the present invention collects spinning product images through a high-resolution camera, divides the spinning product images into a plurality of image area blocks through grayscale threshold segmentation, removes the non-shadow area, obtains the shadow area in the spinning product image, distinguishes the shadow area as a convex area or a concave area, and evaluates the quality of the spinning product by constructing a spinning product quality evaluation model, which can effectively solve the problem that the convexity and concavity of the local shadow area cannot be determined during spinning product image recognition, and helps to improve the accuracy and reliability of the evaluation of the spinning quality; can improve production efficiency, reduce labor costs, and at the same time can reduce subjective errors and improve the consistency and accuracy of the evaluation; is easy to timely discover and deal with spinning product quality problems, improve the quality control efficiency on the production line, and reduce the generation of defective products.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the structure of a visual identification system for spinning quality provided in Embodiment 1 of the present invention.

Detailed ways

The following will be combined with the drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

Embodiment 1

FIG1 shows a schematic diagram of the structure of a spinning quality visual recognition system provided by an embodiment of the present invention. As shown in FIG1 , a spinning quality visual recognition system in this embodiment includes a processor, a spinning product image acquisition module and a spinning quality visual recognition module connected to the processor in communication.

The spinning product image acquisition module is used to collect spinning product images through a high-resolution camera;

The spinning quality visual recognition module is used to visually recognize spinning product images and perform quality assessment.

The spinning quality visual recognition module includes an image processing unit, a shadow area segmentation unit, an area detection unit and a spinning quality assessment unit;

The image processing unit is connected to the shadow area segmentation unit and is used for removing noise, enhancing contrast and graying the spinning product image;

The shadow area segmentation unit is connected to the area detection unit, and is used to segment the spinning product image into a plurality of image area blocks through grayscale threshold segmentation, and remove the non-shadow area to obtain the shadow area in the spinning product image;

The area detection unit is connected to the spinning quality evaluation unit and is used to distinguish whether the shadow area is a convex area or a concave area;

The spinning quality evaluation unit is used to evaluate the quality of spinning products by constructing a spinning product quality evaluation model.

The spinning product image is collected by a high-resolution camera, and the spinning product image is segmented into several image area blocks through grayscale threshold segmentation, and the non-shadow area is eliminated to obtain the shadow area in the spinning product image, and the shadow area is distinguished as a convex area or a concave area. The quality of the spinning product is evaluated by constructing a spinning product quality evaluation model, which can effectively solve the problem that the convexity of the local shadow area cannot be determined during spinning product image recognition, and help to improve the accuracy and reliability of the evaluation of the spinning quality; it can improve production efficiency and reduce labor costs, and at the same time it can also reduce subjective errors and improve the consistency and accuracy of the evaluation; it is easy to timely discover and deal with spinning product quality problems, improve the quality control efficiency on the production line, and reduce the generation of defective products.

The shadow area segmentation unit includes a non-shadow area recognition subunit, a non-shadow area segmentation subunit and a shadow area extraction subunit, the non-shadow area recognition subunit is connected to the non-shadow area segmentation subunit, and the non-shadow area segmentation subunit is connected to the shadow area extraction subunit;

The non-shadow area recognition unit is used to recognize the non-shadow area in the spinning product image;

The non-shadow region segmentation subunit is used for segmenting the non-shadow region in the spinning product image;

The shadow area extraction unit is used to extract the remaining image area in the spinning product image, and the remaining image area is the shadow area.

The pixels of the spinning product image have L grayscale levels, where the number of pixels with grayscale level L is L _N , and the total number of pixels of the spinning product image is:

Where: Z _N is the total number of pixels of the spinning product image, L is the number of pixel grayscale representations of the spinning product image, _Li is the number of pixels with grayscale representation i, and L _N is the number of pixels with grayscale representation L.

The non-shadow area segmentation subunit is used to segment the non-shadow area in the spinning product image, divide the spinning product image into several pore image blocks according to the specified pore size of spinning, eliminate the pore image blocks in the non-shadow area according to the spinning target grayscale value, and leave the pore image blocks in the shadow area.

By calculating the grayscale range of the pore image blocks and calculating the mean of the grayscale range of each pore image block as the spinning target grayscale value, the grayscale range of the pore image blocks is arranged in descending order as a range sequence {K _h (i _h ,j _h ),…,K ₂ (i ₂ ,j ₂ ),K ₁ (i ₁ ,j ₁ )}, where K ₁ (i ₁ ,j ₁ ) is the pore image block with the smallest grayscale range value, K ₂ (i ₂ ,j ₂ ) is the pore image block with the second smallest grayscale range value, and K _h (i _h ,j _h ) is the pore image block with the largest grayscale range value; the 40% range sequence with grayscale range from large to small {K 0.4h (i 0.4h ,j _0.4h ),…,K 2 (i 2 ,j 2 ),K 1 (i 1 ,j 1 )} is arranged in descending order as a range sequence {K _h (i _h ,j h ),…,K ₂ (i ₂ ,j ₂ ),K ₁ (i ₁ ,j _{1 )} } )}, where K _0.4h (i _0.4h ,j _0.4h ) is the pore image block whose grayscale range value is 40% of the maximum value; the grayscale range value of the spinning target grayscale value of the remaining range sequence is compared with the spinning target grayscale value, and the pore image blocks whose grayscale range value is greater than the spinning target grayscale value are taken as shadow areas and marked.

By segmenting the spinning product image according to the pore size specified for spinning, the non-shadow area can be accurately segmented, avoiding inaccurate segmentation caused by shadows; by calculating the grayscale range of the pore image block and eliminating the pore image blocks that do not meet the conditions according to the range sequence, the spinning target area, that is, the shadow area, can be effectively extracted; by comparing with the spinning target grayscale value, the pore image block with a grayscale range value greater than the spinning target grayscale value is regarded as the shadow area, reducing the possibility of misjudgment and improving the accuracy of segmentation; the image segmentation and shadow area extraction can be automatically performed, reducing the need for manual intervention and improving the efficiency and consistency of processing.

The region detection unit includes a concave-convex region distinguishing unit, a convex region outputting unit and a concave region outputting unit, and the concave-convex region distinguishing unit is connected to the convex region outputting unit and the concave region outputting unit respectively;

The concave-convex region discrimination unit is used to distinguish the concave-convexity of the shadow region by calculating the grayscale mean of the pore image block in the shadow region and comparing the grayscale value of the pore image block with the grayscale mean;

The convex region output unit is used to output the pore image block in the convex region;

The concave region output unit is used to output the pore image block in the concave region.

The concave-convex region discrimination unit is used to calculate the grayscale mean of the pore image block in the shadow area, and compare the grayscale value of the pore image block with the grayscale mean to distinguish the concave-convexity of the shadow area. The grayscale value of the pore image block is compared with the grayscale mean in the following way:

when When , the pore image block is a convex area;

when When , the pore image block is a concave area;

Where s _Hq is the gray value of the qth pore image block, is the grayscale mean of the pore image block.

By calculating the grayscale mean of the pore image block in the shadow area and comparing the grayscale value of the pore image block with the grayscale mean, the concave and convex areas can be automatically distinguished, which is convenient for avoiding the subjectivity and inconsistency of manual judgment and improving the accuracy of concave and convex area discrimination; by comparing the grayscale values, it is possible to quickly determine whether the pore image block is a concave area or a convex area, which speeds up the speed of concave and convex area discrimination and improves processing efficiency; comparing the grayscale value of the pore image block with the grayscale mean can better reflect the grayscale change of the pore image block, thereby more accurately judging the concave and convexity; the judgment of concave and convexity is based on the relationship between the grayscale value and the grayscale mean, rather than a simple threshold comparison, so it can more reliably judge the concave and convexity.

The spinning quality evaluation unit includes a pore size abnormality analysis unit, a convex region statistics unit and a quality evaluation unit; the pore size abnormality analysis unit and the convex region statistics unit are respectively connected to the quality evaluation unit;

The pore size anomaly analysis unit is used to analyze the pore size anomaly value of the spinning product through the pore size anomaly analysis calculation model;

The convex region statistical unit is used to count and integrate the pore image blocks in the concave region;

The quality evaluation unit is used to evaluate the quality of spun yarn by constructing a spun yarn quality evaluation model.

The pore size anomaly analysis unit is used to extract the pore area in each pore image block in the spinning product image, arrange the pore area of each pore image block from large to small according to the numerical value, and calculate the average pore area of each pore image block, and import the pore area of each pore image block and the average pore area of each pore image block into the pore size anomaly analysis calculation model to calculate the pore size anomaly value of the spinning product, wherein the calculation formula of the anomaly analysis calculation model is:

Where: yf _x is the abnormal value of the pore size of the spinning product, is the mean pore area of each pore image block, P is the total number of pore image blocks, kx _pmax is the maximum pore area in pore image block p, and kx _pmin is the minimum pore area in pore image block p.

It can effectively extract the pore area of each pore image block in the spinning product image and sort them from large to small according to the area, which helps to intuitively understand the size distribution of different pores; by calculating the mean pore area of each pore image block, the average level of pore area can be obtained, which helps to judge the trend and change of the overall pore size; by importing the pore area and the mean pore area into the abnormal analysis calculation model, the pore size abnormal value can be calculated, which can help identify the abnormal pore size, so as to discover and solve the problem in time; the calculation formula can be used to quantitatively evaluate the abnormal pore size, which helps to accurately locate and quantitatively evaluate the pore size abnormality and provide a basis for subsequent processing.

The convex area statistical unit is used to count the pore image blocks in the concave area, obtain the number of pore image blocks contained in the concave area, the area of each pore image block and the average area of the pore image blocks, and import the number of pore image blocks in the concave area, the area of each pore image block and the average area of the pore image blocks into the concave area defect value calculation formula to calculate the concave area defect value in the spinning product, wherein the concave area defect value calculation formula is:

Where: _aqx is the defect value of the concave area, _Sg is the area of the g-th pore image block in the concave area, G is the total number of pore image blocks in the concave area, is the average area of the pore image blocks in the concave area.

By counting the number of pore image blocks in the concave area, the area of each pore image block and the mean area of the pore image blocks, and importing them into the calculation formula for the defect value of the concave area, the defect value of the concave area can be calculated, which is helpful for quantitatively evaluating the quality status of the concave area; by counting the number and area information of the pore image blocks in the concave area, the size distribution and quantity distribution of the concave area can be fully understood, which helps to discover potential defect problems; the mean area of the pore image blocks in the concave area is one of the important indicators to measure the degree of defects in the concave area, and can provide a reference for the calculation of the defect value; through the calculation formula for the defect value of the concave area, the degree of defects in the concave area can be quantified, providing quantitative data support for subsequent quality analysis and improvement.

The quality assessment unit is used to assess the quality of the spinning product by obtaining the abnormal value of the pore size of the spinning product, the total area of the spinning product image of the defect value of the concave area, and the total area of the G pore image blocks in the concave area, and importing the abnormal value of the pore size of the spinning product, the total area of the spinning product image of the defect value of the concave area, and the total area of the G pore image blocks in the concave area into the spinning product quality assessment model, wherein the formula of the spinning product quality assessment model is:

Where: zl _f is the quality index of the spinning product, aq _x is the defect value of the concave area, yf _x is the pore size abnormality value of the spinning product, S _z is the total area of the spinning product image with defect value in the concave area, and S _g is the total area of G pore image blocks in the concave area.

By introducing the abnormal values of pore size of spinning products, defect values of concave areas, total area of spinning product images and total area of pore image blocks in the concave areas into the spinning product quality assessment model, multiple quality indicators of spinning products are comprehensively considered, which helps to understand the influence of each indicator on the quality of spinning products; by quantitatively evaluating the quality of spinning products, quality problems can be discovered and identified in time, and improvement measures can be determined, which helps to guide adjustments and improvements in the production process and improve the quality level of spinning products; by quantitatively evaluating and analyzing the quality of spinning products, problems can be discovered in time and effective measures can be taken, thereby improving production efficiency and product quality, reducing the rate of defective products and enhancing corporate competitiveness.

The embodiment of the present invention collects spinning product images through a high-resolution camera, divides the spinning product images into several image area blocks through grayscale threshold segmentation, removes non-shadow areas, obtains shadow areas in the spinning product images, distinguishes the shadow areas as convex areas or concave areas, and evaluates the quality of the spinning products by constructing a spinning product quality evaluation model. This can effectively solve the problem that the convexity and concavity of the local shadow area cannot be determined during spinning product image recognition, and help to improve the accuracy and reliability of the evaluation of the quality of the spinning products; it can improve production efficiency and reduce labor costs, and at the same time can also reduce subjective errors and improve the consistency and accuracy of the evaluation; it is easy to timely discover and deal with spinning product quality problems, improve the quality control efficiency on the production line, and reduce the generation of defective products.

The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (8)

1. The spinning quality visual recognition system comprises a processor, a spinning quality image acquisition module and a spinning quality visual recognition module, wherein the spinning quality visual recognition module is in communication connection with the processor, and the spinning quality image acquisition module is used for acquiring spinning quality images through a high-resolution camera; the spinning quality visual recognition module is used for visually recognizing a spinning product image and carrying out quality assessment, and is characterized by comprising an image processing unit, a shadow area segmentation unit, an area detection unit and a spinning quality assessment unit, wherein the spinning quality assessment unit is used for assessing the quality of the spinning product by constructing a spinning quality assessment model, and by acquiring the pore size abnormal value of the spinning product, the total area of a defect value spinning product image in a concave area and the total area of G pore image blocks in the concave area, and introducing the pore size abnormal value of the spinning product, the total area of the defect value spinning product image in the concave area and the total area of G pore image blocks in the concave area into the spinning product quality assessment model, and the quality of the spinning product is assessed, wherein the spinning product quality assessment model has the formula:

Wherein: zl _f is a quality index of the spinning product, aq _x is a defect value of a concave area, yf _x is an abnormal value of pore size of the spinning product, S _z is a total area of images of the spinning product with the defect value of the concave area, and S _g is a total area of G pore image blocks in the concave area.

2. The spinning quality visual recognition system according to claim 1, wherein the image processing unit is configured to perform noise removal, contrast enhancement and graying processing on the spun yarn image;

The shadow region segmentation unit is used for segmenting the spinning product image into a plurality of image region blocks through gray threshold segmentation, and removing the non-shadow region to obtain a shadow region in the spinning product image;

the area detection unit is used for distinguishing the shadow area into a convex area or a concave area;

The spinning quality evaluation unit is used for evaluating the quality of the spinning product by constructing a spinning quality evaluation model.

3. The spinning quality visual recognition system according to claim 1, wherein the shadow region segmentation unit includes a non-shadow region recognition subunit, a non-shadow region segmentation subunit, and a shadow region extraction subunit, wherein,

The non-shadow area identification unit is used for identifying a non-shadow area in the spinning product image;

The non-shadow region segmentation subunit is used for segmenting a non-shadow region in the spinning product image;

The shadow region extraction unit is used for extracting a residual image region in the spinning product image, wherein the residual image region is the shadow region.

4. A spinning quality visual recognition system according to claim 3, wherein the non-shadow region segmentation subunit is configured to segment a non-shadow region in the spun yarn image, segment the spun yarn image into a plurality of pore image blocks according to a spinning specified pore size, reject the pore image blocks of the non-shadow region according to a spinning target gray value, and leave the pore image blocks of the shadow region.

5. A spinning quality visual recognition system according to claim 3, wherein the spinning target gray value is obtained by calculating the gray level range average value of each pore image block, and the gray level ranges of the pore image blocks are arranged into a range sequence {K_h(i_h,j_h),…,K₂(i₂,j₂),K₁(i₁,j₁)}, in the order from the top to the bottom, wherein K ₁(i₁,j₁) is the pore image block with the smallest gray level range value, K ₂(i₂,j₂) is the pore image block with the second smallest gray level range value, and K _h(i_h,j_h) is the pore image block with the largest gray level range value; removing the pore image blocks of the sequence {K_0.4h(i_0.4h,j_0.4h),…,K₂(i₂,j₂),K₁(i₁,j₁)} of the full range of the gray level from large to small, wherein K _0.4h(i_0.4h,h_0.4h) is the pore image block of which the full range value is 40% of the maximum value; and comparing the gray level difference value of the spinning target gray level value of the residual level difference sequence with the spinning target gray level value, taking a pore image block with the gray level difference value larger than the spinning target gray level value as a shadow area, and marking.

6. The spinning quality visual recognition system according to claim 1, wherein the area detection unit includes a concave-convex area discrimination unit, a convex area output unit, and a concave area output unit; the concave-convex area distinguishing unit is used for comparing the gray value of the pore image block with the gray average value by calculating the gray average value of the pore image block in the shadow area, and distinguishing the concave-convex of the shadow area, wherein the comparison mode of the gray value of the pore image block and the gray average value is as follows:

When (when) When the aperture image block is a convex part area;

When (when) When the aperture image block is a concave area;

Wherein s _Hq is the gray value of the qth aperture image block, Is the gray average of the pore image block.

7. The spinning quality visual recognition system according to claim 1, wherein the spinning quality evaluation unit comprises a pore size abnormality analysis unit, a convex part area statistics unit and a quality evaluation unit, wherein the pore size abnormality analysis unit is used for extracting pore areas in pore image blocks in a spinning product image, arranging the pore areas of the pore image blocks according to the numerical values from large to small, calculating a pore area average value of the pore image blocks, and introducing the pore areas of the pore image blocks and the pore area average value of the pore image blocks into a pore size abnormality analysis calculation model to calculate pore size abnormality values of the spinning product, wherein a calculation formula of the abnormality analysis calculation model is as follows:

Wherein: yf _x is the abnormal value of the pore size of the spinning product, For the average pore area of each pore image block, P is the total number of pore image blocks, kx _pmax is the maximum pore area in pore image block P, and kx _pmin is the minimum pore area in pore image block P.

8. The spinning quality visual recognition system according to claim 7, wherein the convex portion region statistics unit is configured to count aperture image blocks in a concave portion region, obtain a number of aperture image blocks, an area of each aperture image block, and an area average value of the aperture image blocks included in the concave portion region, and import the number of aperture image blocks, the area of each aperture image block, and the area average value of the aperture image blocks in the concave portion region into a concave portion region defect value calculation formula, and calculate a defect value of the concave portion region in the spun yarn, wherein the concave portion region defect value calculation formula is:

Wherein: aq _x is a defective value of a recess region, S _g is an area of a G-th aperture image block in the recess region, G is a total number of aperture image blocks in the recess region, Is the average value of the area of the pore image block in the concave part area.