基于非线性扩散和多特征融合的提花针织物疵点检测  

Defect detection of jacquard knitted fabrics based on nonlinear diffusion and multi-feature fusion

作　　者：史伟民[1] 简强 李建强[2] 汝欣 彭来湖[1,2] SHI Weimin;JIAN Qiang;LI Jianqiang;RU Xin;PENG Laihu(Key Laboratory of Modern Textile Machinery&Technology of Zhejiang Province,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;Research Institute of Zhejiang Sci-Tech University in Longgang,Wenzhou,Zhejiang 325802,China)

机构地区：[1]浙江理工大学浙江省现代纺织装备技术重点实验室,浙江杭州310018 [2]浙江理工大学龙港研究院有限公司,浙江温州325802

出　　处：《纺织学报》2023年第7期86-94,共9页Journal of Textile Research

基　　金：国家重点研发计划重点专项课题(2017YFB1304005);浙江省公益技术研究计划项目(LGG21E050024);浙江理工大学科研启动基金项目(18022224-Y);浙江省博士后科研项目特别资助项目(ZJ2020004)。

摘　　要：为解决提花针织物的复杂纹理在疵点检测过程中易造成检测干扰和疵点误判的问题,提出一种基于非线性扩散和多特征融合的疵点检测方法。采用改进PM模型对提花针织物的花纹和强纹理边缘进行抑制,首先利用梯度差异将疵点图像分为纹理区域及疵点区域,然后结合各区域特点选择对应的扩散方程,依据梯度矩阵计算概率子集、相关准则来确定梯度阈值,实现分区域扩散。根据提花针织物的纹理分布特性,提取改进局部二值算法(LBP)、局部熵、局部相关性等表征参数,然后进行去邻域归一化和多特征融合进一步突出疵点区域,最后利用区域生长法定位分割出疵点形态。实验验证了本文预处理方法及疵点检测方法的有效性,通过与其它预处理算法和疵点检测算法进行对比,结果表明本文算法的检测效果最好,对正常织物图像的误检率为3.3%,对含疵点织物图像检测的准确率为98.6%。Objective Supervision and inspection are important parts in quality control of the knitted fabric production process.The defect detection by automation and machine vision technology can effectively improve the detection efficiency.Jacquard knitted fabrics have prominent yarn edges,obvious loop characteristics and patterns,which have a strong interference to the defect detection process.Therefore,it is necessary to design an effective and accurate pretreatment and defect detection method for the complex texture of jacquard knitted fabrics.Method Improved PM(perona-malik)model was adopted to suppress the strong texture edge of jacquard knitted fabric.Firstly,the image was divided into texture and defect region by gradient difference before selecting the corresponding diffusion equation.The gradient threshold was determined according to the probability subset calculated by the gradient matrix and the relevant criteria to achieve regional diffusion.According to the texture distribution characteristics,the improved local binary pattern(LBP),entropy and correlation were extracted,and then the defect regions were further highlighted by neighborhood normalization and multi-feature fusion.Finally,the defect morphology was located and segmented by region growth method.Results The effectiveness of the preprocessing method and the defect detection method for texture suppression was experimental investigated and analyzed,and defect information extraction of jacquard knitted fabric was demonstrated exhaustively.In addition,several different preprocessing algorithms and defect detection algorithms were compared and demonstrated.Comparison of defect image before and after preprocessing showed that the gray fluctuation amplitude of the image after preprocessing was smaller and the texture distribution was more concentrated.It was seen from the pretreatment experimental images and from the comparison effect with other preprocessing algorithms that the abrupt change of the texture edge area was still obvious,and the yarn spacing ar

关 键 词：提花针织物 PM模型 扩散方程 梯度阈值 改进局部二值算法 去邻域归一化 多特征融合 

分 类 号：TS181.9[轻工技术与工程—纺织材料与纺织品设计] TP391.4[轻工技术与工程—纺织科学与工程]