CN118247231A - Spinning quality visual identification system - Google Patents

Spinning quality visual identification system Download PDF

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CN118247231A
CN118247231A CN202410300605.6A CN202410300605A CN118247231A CN 118247231 A CN118247231 A CN 118247231A CN 202410300605 A CN202410300605 A CN 202410300605A CN 118247231 A CN118247231 A CN 118247231A
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pore
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value
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CN118247231B (en
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陈勇
柏学军
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WUJIANG XINFENG WEAVING CO Ltd
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Lechang Hengfa Textile Enterprise Co ltd
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0004Industrial image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/10Segmentation; Edge detection
    • G06T7/136Segmentation; Edge detection involving thresholding
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    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/60Analysis of geometric attributes
    • G06T7/62Analysis of geometric attributes of area, perimeter, diameter or volume
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/60Analysis of geometric attributes
    • G06T7/64Analysis of geometric attributes of convexity or concavity
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30108Industrial image inspection
    • G06T2207/30124Fabrics; Textile; Paper
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30168Image quality inspection
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02PCLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30Computing systems specially adapted for manufacturing

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Abstract

本发明涉及视觉识别领域,具体公开了一种纺纱质量视觉识别系统,包括处理器以及与处理器通讯相连的纺纱品图像获取模块以及纺纱质量视觉识别模块,用于解决纺纱品图像识别时,无法确定局部阴影区域的凹凸性的问题;本发明通过高分辨率的相机采集纺纱品图像,对纺纱品图像进行灰度阈值分割,将纺纱品图像分割为若干图像区域块,并将非阴影区域进行剔除,得到纺纱品图像中的阴影区域,区分阴影区域为凸部区域或是凹部区域,通过构建纺纱品质量评估模型对纺纱品的质量进行评估,能够有效地解决纺纱品图像识别时无法确定局部阴影区域凹凸性的问题,并有助于提高对纺纱品质量的评估准确性和可靠性。

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

一种纺纱质量视觉识别系统A visual recognition system for spinning quality

技术领域Technical Field

本发明涉及视觉识别领域,更具体地说,本发明涉及一种纺纱质量视觉识别系统。The present invention relates to the field of visual recognition, and more particularly to a visual recognition system for spinning quality.

背景技术Background technique

纺纱品的质量检测,传统的检测方法往往通过操作工人的眼睛进行瑕疵识别,但这种检测方法存在漏检率高、工作强度大以及受操作工人主观因素影响较大的问题,这些问题会导致纺织品的质量下降,从而影响顾客的触感舒适度。将视觉识别应用于纺纱品的瑕疵识别,提高了瑕疵识别的精确度和效率,并增强了对瑕疵种类的分辨能力。目前对纺纱品进行视觉识别时,发现存在装饰的纺纱品,其部分装饰区域常需要统一进行凸部设计,但由于织造过程中所受到的机械摩擦或者纤维之间排列不均匀,而导致纺纱品存在不同程度的凹凸,使得图像的清晰度存在缺陷,将图像中存在较多的阴影区域进行对比,无法确定局部阴影区域凹凸性,从而影响顾客的舒适度,并对纺纱品图像以及外观的美观度造成了影响,为了解决上述问题,现提供一种技术方案。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.

发明内容Summary of the invention

为了克服现有技术的上述缺陷,本发明提供一种纺纱质量视觉识别系统,通过高分辨率的相机采集纺纱品图像,通过灰度阈值分割,将纺纱品图像分割为若干图像区域块,并将非阴影区域进行剔除,得到纺纱品图像中的阴影区域,区分阴影区域为凸部区域或是凹部区域,通过构建纺纱品质量评估模型对纺纱品的质量进行评估,能够有效地解决纺纱品图像识别时无法确定局部阴影区域凹凸性的问题,并有助于提高对纺纱品质量的评估准确性和可靠性,以解决上述背景技术中提出的问题。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.

为实现上述目的,本发明提供如下技术方案:To achieve the above object, the present invention provides the following technical solutions:

一种纺纱质量视觉识别系统,包括处理器以及与处理器通讯相连的纺纱品图像获取模块以及纺纱质量视觉识别模块,纺纱品图像获取模块用于通过高分辨率的相机采集纺纱品图像;纺纱质量视觉识别模块用于对纺纱品图像进行视觉识别并进行质量评估,纺纱质量视觉识别模块包括图像处理单元、阴影区域分割单元、区域检测单元以及纺纱质量评估单元,纺纱质量评估单元用于通过构建纺纱品质量评估模型对纺纱品的质量进行评估,通过获取纺纱品的孔隙尺寸异常值、凹部区域瑕疵值纺纱品图像总面积以及凹部区域中G个孔隙图像块的总面积,并将纺纱品的孔隙尺寸异常值、凹部区域瑕疵值纺纱品图像总面积以及凹部区域中G个孔隙图像块的总面积导入至纺纱品质量评估模型中,对纺纱品的质量进行评估,其中,纺纱品质量评估模型的公式为: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:

式中:zlf为纺纱品质量指标,aqx为凹部区域瑕疵值,yfx为纺纱品的孔隙尺寸异常值,Sz为凹部区域瑕疵值纺纱品图像总面积,Sg为凹部区域中G个孔隙图像块的总面积。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.

作为本发明的进一步方案,图像处理单元用于对纺纱品图像进行去除噪声、增强对比度以及灰度化处理;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;

阴影区域分割单元用于通过灰度阈值分割,将纺纱品图像分割为若干图像区域块,并将非阴影区域进行剔除,得到纺纱品图像中的阴影区域;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;

区域检测单元用于区分阴影区域为凸部区域或是凹部区域;The region detection unit is used to distinguish whether the shadow region is a convex region or a concave region;

纺纱质量评估单元用于通过构建纺纱品质量评估模型对纺纱品的质量进行评估。The spinning quality evaluation unit is used to evaluate the quality of spinning products by constructing a spinning product quality evaluation model.

作为本发明的进一步方案,阴影区域分割单元包括非阴影区域识别子单元、非阴影区域分割子单元以及阴影区域提取子单元,其中,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:

非阴影区域识别单元用于对纺纱品图像中的非阴影区域进行识别;The non-shadow area recognition unit is used to recognize the non-shadow area in the spinning product image;

非阴影区域分割子单元用于将纺纱品图像中的非阴影区域分割;The non-shadow region segmentation subunit is used for segmenting the non-shadow region in the spinning product image;

阴影区域提取单元用于提取纺纱品图像中剩余图像区域,剩余图像区域即为阴影区域。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.

作为本发明的进一步方案,非阴影区域分割子单元用于将纺纱品图像中的非阴影区域分割,将纺纱品图像按照纺纱规定孔隙尺寸分割为若干孔隙图像块,根据纺纱目标灰度值剔除非阴影区域的孔隙图像块,留下阴影区域的孔隙图像块。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.

作为本发明的进一步方案,纺纱目标灰度值通过计算各孔隙图像块的灰度极差均值得到,将孔隙图像块的灰度极差按照由大至小的顺序排列为极差序列{Kh(ih,jh),…,K2(i2,j2),K1(i1,j1)},其中,K1(i1,j1)为灰度极差数值最小的孔隙图像块,K2(i2,j2)为灰度极差数值第二小的孔隙图像块,Kh(ih,jh)为灰度极差数值最大的孔隙图像块;将灰度极差由大至小的40%极差序列{K0.4h(i0.4h,j0.4h),…,K2(i2,j2),K1(i1,j1)}的孔隙图像块剔除,其中,K0.4h(i0.4h,j0.4h)为灰度极差数值为最大值的40%的孔隙图像块;将剩余极差序列的纺纱目标灰度值的灰度极差数值,与纺纱目标灰度值进行比较,将灰度极差数值大于纺纱目标灰度值的孔隙图像块作为阴影区域,并进行标识。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.

作为本发明的进一步方案,区域检测单元包括凹凸区域判别单元、凸部区域输出单元以及凹部区域输出单元;凹凸区域判别单元用于通过计算阴影区域内孔隙图像块的灰度均值,将孔隙图像块的灰度值与灰度均值进行比较,区分阴影区域的凹凸性,孔隙图像块的灰度值与灰度均值进行比较的方式为: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:

时,则该孔隙图像块为凸部区域;when When , the pore image block is a convex area;

时,则该孔隙图像块为凹部区域;when When , the pore image block is a concave area;

其中,sHq为第q个孔隙图像块的灰度值,为孔隙图像块的灰度均值。Where s Hq is the gray value of the qth pore image block, is the grayscale mean of the pore image block.

作为本发明的进一步方案,纺纱质量评估单元包括孔隙尺寸异常分析单元、凸部区域统计单元以及品质评估单元,孔隙尺寸异常分析单元用于提取纺纱品图像中各孔隙图像块内的孔隙面积,将各孔隙图像块的孔隙面积按照数值由大至小进行排列,并计算各孔隙图像块的孔隙面积均值,将各孔隙图像块的孔隙面积以及各孔隙图像块的孔隙面积均值导入至孔隙尺寸异常分析计算模型中,进行纺纱品的孔隙尺寸异常值计算,其中,异常分析计算模型的计算公式为: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:

式中:yfx为纺纱品的孔隙尺寸异常值,为各孔隙图像块的孔隙面积均值,P为孔隙图像块的总数,kxpmax为孔隙图像块p中的最大孔隙面积,kxpmin为孔隙图像块p中的最小孔隙面积。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.

作为本发明的进一步方案,凸部区域统计单元用于统计凹部区域的孔隙图像块,获取凹部区域中所包含的孔隙图像块个数、各孔隙图像块面积以及孔隙图像块面积均值,将凹部区域中孔隙图像块个数、各孔隙图像块面积以及孔隙图像块面积均值导入至凹部区域瑕疵值计算公式中,进行纺纱品中凹部区域瑕疵值的计算,其中,凹部区域瑕疵值计算公式为: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:

式中:aqx为凹部区域瑕疵值,Sg为凹部区域中第g个孔隙图像块的面积,G为凹部区域中的孔隙图像块总数,为凹部区域中孔隙图像块面积均值。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.

本发明一种纺纱质量视觉识别系统的技术效果和优点:本发明通过高分辨率的相机采集纺纱品图像,通过灰度阈值分割,将纺纱品图像分割为若干图像区域块,并将非阴影区域进行剔除,得到纺纱品图像中的阴影区域,区分阴影区域为凸部区域或是凹部区域,通过构建纺纱品质量评估模型对纺纱品的质量进行评估,能够有效地解决纺纱品图像识别时无法确定局部阴影区域凹凸性的问题,并有助于提高对纺纱品质量的评估准确性和可靠性;能够提高生产效率,降低人力成本,同时还能够减少主观误差,提高评估的一致性和准确性;便于及时发现和处理纺纱品质量问题,提高生产线上的质量控制效率,减少不合格品的产生。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.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

图1为本发明实施例一提供的一种纺纱质量视觉识别系统的结构示意图。FIG1 is a schematic diagram of the structure of a visual identification system for spinning quality provided in Embodiment 1 of the present invention.

具体实施方式Detailed ways

下面将结合本发明中的附图,对本发明中的技术方案进行清楚、完整地描述,显然,所描述的技术方案仅仅是本发明一部分,而不是全部。基于本发明中的技术方案,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他技术方案,都属于本发明保护的范围。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.

实施例一Embodiment 1

图1示出了本发明实施例一提供的一种纺纱质量视觉识别系统的结构示意图。如图1所示,本实施例中的一种纺纱质量视觉识别系统,一种纺纱质量视觉识别系统,包括处理器以及与处理器通讯相连的纺纱品图像获取模块以及纺纱质量视觉识别模块。其中,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.

纺纱品图像获取模块用于通过高分辨率的相机采集纺纱品图像;The spinning product image acquisition module is used to collect spinning product images through a high-resolution camera;

纺纱质量视觉识别模块用于对纺纱品图像进行视觉识别并进行质量评估。The spinning quality visual recognition module is used to visually recognize spinning product images and perform quality assessment.

纺纱质量视觉识别模块包括图像处理单元、阴影区域分割单元、区域检测单元以及纺纱质量评估单元;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;

图像处理单元与阴影区域分割单元相连接,用于对纺纱品图像进行去除噪声、增强对比度以及灰度化处理;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;

阴影区域分割单元与区域检测单元相连接,用于通过灰度阈值分割,将纺纱品图像分割为若干图像区域块,并将非阴影区域进行剔除,得到纺纱品图像中的阴影区域;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;

区域检测单元与纺纱质量评估单元相连接,用于区分阴影区域为凸部区域或是凹部区域;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;

纺纱质量评估单元用于通过构建纺纱品质量评估模型对纺纱品的质量进行评估。The spinning quality evaluation unit is used to evaluate the quality of spinning products by constructing a spinning product quality evaluation model.

通过高分辨率的相机采集纺纱品图像,通过灰度阈值分割,将纺纱品图像分割为若干图像区域块,并将非阴影区域进行剔除,得到纺纱品图像中的阴影区域,区分阴影区域为凸部区域或是凹部区域,通过构建纺纱品质量评估模型对纺纱品的质量进行评估,能够有效地解决纺纱品图像识别时无法确定局部阴影区域凹凸性的问题,并有助于提高对纺纱品质量的评估准确性和可靠性;能够提高生产效率,降低人力成本,同时还能够减少主观误差,提高评估的一致性和准确性;便于及时发现和处理纺纱品质量问题,提高生产线上的质量控制效率,减少不合格品的产生。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.

阴影区域分割单元包括非阴影区域识别子单元、非阴影区域分割子单元以及阴影区域提取子单元,非阴影区域识别子单元与非阴影区域分割子单元相连接,非阴影区域分割子单元与阴影区域提取子单元相连接;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;

非阴影区域识别单元用于对纺纱品图像中的非阴影区域进行识别;The non-shadow area recognition unit is used to recognize the non-shadow area in the spinning product image;

非阴影区域分割子单元用于将纺纱品图像中的非阴影区域分割;The non-shadow region segmentation subunit is used for segmenting the non-shadow region in the spinning product image;

阴影区域提取单元用于提取纺纱品图像中剩余图像区域,剩余图像区域即为阴影区域。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.

其中,纺纱品图像的像素共有L级灰度级表示,其中,灰度级表示为L的像素数量为LN,由此得到纺纱品图像的像素总数为: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:

式中:ZN为纺纱品图像的像素总数,L为纺纱品图像的像素灰度级表示个数,Li为灰度级表示为i的像素数量,LN为灰度级表示为L的像素数量。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.

非阴影区域分割子单元用于将纺纱品图像中的非阴影区域分割,将纺纱品图像按照纺纱规定孔隙尺寸分割为若干孔隙图像块,根据纺纱目标灰度值剔除非阴影区域的孔隙图像块,留下阴影区域的孔隙图像块。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.

通过计算孔隙图像块的灰度极差,并计算各孔隙图像块的灰度极差均值作为纺纱目标灰度值,将孔隙图像块的灰度极差按照由大至小的顺序排列为极差序列{Kh(ih,jh),…,K2(i2,j2),K1(i1,j1)},其中,K1(i1,j1)为灰度极差数值最小的孔隙图像块,K2(i2,j2)为灰度极差数值第二小的孔隙图像块,Kh(ih,jh)为灰度极差数值最大的孔隙图像块;将灰度极差由大至小的40%极差序列{K0.4h(i0.4h,j0.4h),…,K2(i2,j2),K1(i1,j1)}的孔隙图像块剔除,其中,K0.4h(i0.4h,j0.4h)为灰度极差数值为最大值的40%的孔隙图像块;将剩余极差序列的纺纱目标灰度值的灰度极差数值,与纺纱目标灰度值进行比较,将灰度极差数值大于纺纱目标灰度值的孔隙图像块作为阴影区域,并进行标识。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.

通过根据纺纱规定的孔隙尺寸分割纺纱品图像,能够精确地分割出非阴影区域,避免了因阴影而引起的不准确分割;通过计算孔隙图像块的灰度极差,并根据极差序列剔除不符合条件的孔隙图像块,能够有效地提取出纺纱目标区域,即阴影区域;通过与纺纱目标灰度值进行比较,将灰度极差数值大于纺纱目标灰度值的孔隙图像块作为阴影区域,减少了误判的可能性,提高了分割的准确性;能够自动进行图像分割和阴影区域提取的处理,减少了人工干预的需求,提高了处理的效率和一致性。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.

区域检测单元包括凹凸区域判别单元、凸部区域输出单元以及凹部区域输出单元,凹凸区域判别单元与凸部区域输出单元以及凹部区域输出单元分别相连接;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;

凹凸区域判别单元用于通过计算阴影区域内孔隙图像块的灰度均值,将孔隙图像块的灰度值与灰度均值进行比较,区分阴影区域的凹凸性;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;

凸部区域输出单元用于输出凸部区域内的孔隙图像块;The convex region output unit is used to output the pore image block in the convex region;

凹部区域输出单元用于输出凹部区域内的孔隙图像块。The concave region output unit is used to output the pore image block in the concave region.

凹凸区域判别单元用于计算阴影区域内孔隙图像块的灰度均值,将孔隙图像块的灰度值与灰度均值进行比较,用以区分阴影区域的凹凸性,孔隙图像块的灰度值与灰度均值进行比较的方式为: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:

时,则该孔隙图像块为凸部区域;when When , the pore image block is a convex area;

时,则该孔隙图像块为凹部区域;when When , the pore image block is a concave area;

其中,sHq为第q个孔隙图像块的灰度值,为孔隙图像块的灰度均值。Where s Hq is the gray value of the qth pore image block, is the grayscale mean of the pore image block.

通过计算阴影区域内孔隙图像块的灰度均值,并将孔隙图像块的灰度值与灰度均值进行比较,能够自动地区分出凹凸区域,便于避免人工判断的主观性和不一致性,提高凹凸区域判别的准确性;通过灰度值的比较,能够快速地判断出孔隙图像块是凹部区域还是凸部区域,加快了凹凸区域判别的速度,提高了处理效率;将孔隙图像块的灰度值与灰度均值进行比较,能够更好地反映孔隙图像块的灰度变化情况,从而更准确地判断凹凸性;凹凸性的判断是基于灰度值与灰度均值之间的关系,而不是简单的阈值比较,因此能够更可靠地进行凹凸性判别。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.

纺纱质量评估单元包括孔隙尺寸异常分析单元、凸部区域统计单元以及品质评估单元;孔隙尺寸异常分析单元以及凸部区域统计单元分别与品质评估单元相连接;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;

孔隙尺寸异常分析单元用于通过孔隙尺寸异常分析计算模型,分析纺纱品的孔隙尺寸异常值;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;

凸部区域统计单元用于统计并整合凹部区域的孔隙图像块;The convex region statistical unit is used to count and integrate the pore image blocks in the concave region;

品质评估单元用于通过构建纺纱品质量评估模型对纺纱品的质量进行评估。The quality evaluation unit is used to evaluate the quality of spun yarn by constructing a spun yarn quality evaluation model.

孔隙尺寸异常分析单元用于提取纺纱品图像中各孔隙图像块内的孔隙面积,将各孔隙图像块的孔隙面积按照数值由大至小进行排列,并计算各孔隙图像块的孔隙面积均值,将各孔隙图像块的孔隙面积以及各孔隙图像块的孔隙面积均值导入至孔隙尺寸异常分析计算模型中,进行纺纱品的孔隙尺寸异常值计算,其中,异常分析计算模型的计算公式为: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:

式中:yfx为纺纱品的孔隙尺寸异常值,为各孔隙图像块的孔隙面积均值,P为孔隙图像块的总数,kxpmax为孔隙图像块p中的最大孔隙面积,kxpmin为孔隙图像块p中的最小孔隙面积。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.

能够有效提取纺纱品图像中各孔隙图像块的孔隙面积,并按照面积由大到小进行排序,有助于直观地了解不同孔隙的大小分布情况;通过计算各孔隙图像块的孔隙面积均值,能够得到孔隙面积的平均水平,有助于判断整体孔隙尺寸的趋势和变化;通过导入孔隙面积和孔隙面积均值至异常分析计算模型中,能够进行孔隙尺寸异常值的计算,能够帮助识别出孔隙尺寸异常的情况,从而及时发现并解决问题;通过计算公式能够对孔隙尺寸的异常情况进行量化评估,有助于精确定位和定量评估孔隙尺寸异常,为后续处理提供依据。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.

凸部区域统计单元用于统计凹部区域的孔隙图像块,获取凹部区域中所包含的孔隙图像块个数、各孔隙图像块面积以及孔隙图像块面积均值,将凹部区域中孔隙图像块个数、各孔隙图像块面积以及孔隙图像块面积均值导入至凹部区域瑕疵值计算公式中,进行纺纱品中凹部区域瑕疵值的计算,其中,凹部区域瑕疵值计算公式为: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:

式中:aqx为凹部区域瑕疵值,Sg为凹部区域中第g个孔隙图像块的面积,G为凹部区域中的孔隙图像块总数,为凹部区域中孔隙图像块面积均值。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.

通过统计凹部区域的孔隙图像块个数、各孔隙图像块面积以及孔隙图像块面积均值,并导入凹部区域瑕疵值计算公式中,能够计算出凹部区域的瑕疵值,有助于定量评估凹部区域的质量状况;通过统计凹部区域中孔隙图像块的个数和面积信息,能够全面了解凹部区域的尺寸分布和数量分布情况,帮助发现潜在的瑕疵问题;凹部区域中孔隙图像块面积均值是衡量凹部区域瑕疵程度的重要指标之一,能够为瑕疵值的计算提供参考依据;通过凹部区域瑕疵值计算公式,能够将凹部区域的瑕疵程度定量化,为后续的质量分析和改进提供了量化的数据支持。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.

品质评估单元用于通过获取纺纱品的孔隙尺寸异常值、凹部区域瑕疵值纺纱品图像总面积以及凹部区域中G个孔隙图像块的总面积,并将纺纱品的孔隙尺寸异常值、凹部区域瑕疵值纺纱品图像总面积以及凹部区域中G个孔隙图像块的总面积导入至纺纱品质量评估模型中,对纺纱品的质量进行评估,其中,纺纱品质量评估模型的公式为: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:

式中:zlf为纺纱品质量指标,aqx为凹部区域瑕疵值,yfx为纺纱品的孔隙尺寸异常值,Sz为凹部区域瑕疵值纺纱品图像总面积,Sg为凹部区域中G个孔隙图像块的总面积。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.

通过将纺纱品的孔隙尺寸异常值、凹部区域瑕疵值、纺纱品图像总面积以及凹部区域中孔隙图像块的总面积导入到纺纱品质量评估模型中,综合考量了纺纱品的多个质量指标,有助于理解各指标对纺纱品质量的影响程度;通过定量评估纺纱品质量,能够及时发现和识别质量问题,并确定改进措施,有助于指导生产过程中的调整和改进,提高纺纱品的质量水平;通过对纺纱品质量进行定量评估和分析,能够及时发现问题并采取有效措施,从而提高生产效率和产品质量,降低不合格品率,提升企业竞争力。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.

本发明实施例通过高分辨率的相机采集纺纱品图像,通过灰度阈值分割,将纺纱品图像分割为若干图像区域块,并将非阴影区域进行剔除,得到纺纱品图像中的阴影区域,区分阴影区域为凸部区域或是凹部区域,通过构建纺纱品质量评估模型对纺纱品的质量进行评估,能够有效地解决纺纱品图像识别时无法确定局部阴影区域凹凸性的问题,并有助于提高对纺纱品质量的评估准确性和可靠性;能够提高生产效率,降低人力成本,同时还能够减少主观误差,提高评估的一致性和准确性;便于及时发现和处理纺纱品质量问题,提高生产线上的质量控制效率,减少不合格品的产生。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.

以上所述,仅为本申请的具体实施方式,但本申请的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本申请揭露的技术范围内,可轻易想到变化或替换,都应涵盖在本申请的保护范围之内。因此,本申请的保护范围应以所述权利要求的保护范围为准。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 {Kh(ih,jh),…,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(ih,jh) is the pore image block with the largest gray level range value; removing the pore image blocks of the sequence {K0.4h(i0.4h,j0.4h),…,K2(i2,j2),K1(i1,j1)} of the full range of the gray level from large to small, wherein K 0.4h(i0.4h,h0.4h) 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.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102967606A (en) * 2012-11-02 2013-03-13 海宁市科威工业电子科技有限公司 Textile machine fabric defect visual inspection system
CN103471973A (en) * 2013-09-05 2013-12-25 东华大学 Method for determining porosity characteristics of fabric through image method
CN107977954A (en) * 2017-10-12 2018-05-01 常州信息职业技术学院 Textile flaw detection method based on local optimum analysis
WO2019104767A1 (en) * 2017-11-28 2019-06-06 河海大学常州校区 Fabric defect detection method based on deep convolutional neural network and visual saliency
CN117274133A (en) * 2022-06-09 2023-12-22 鸿海精密工业股份有限公司 Defect detection method, electronic device and storage medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102967606A (en) * 2012-11-02 2013-03-13 海宁市科威工业电子科技有限公司 Textile machine fabric defect visual inspection system
CN103471973A (en) * 2013-09-05 2013-12-25 东华大学 Method for determining porosity characteristics of fabric through image method
CN107977954A (en) * 2017-10-12 2018-05-01 常州信息职业技术学院 Textile flaw detection method based on local optimum analysis
WO2019104767A1 (en) * 2017-11-28 2019-06-06 河海大学常州校区 Fabric defect detection method based on deep convolutional neural network and visual saliency
CN117274133A (en) * 2022-06-09 2023-12-22 鸿海精密工业股份有限公司 Defect detection method, electronic device and storage medium

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