基于多尺度Laws纹理能量和低秩分解的织物疵点检测算法  

Fabric defects detection algorithm based on multi-scale Laws texture energy and low-rank decomposition

作　　者：王振华 张周强 昝杰 刘江浩 WANG Zhenhua;ZHANG Zhouqiang;ZAN Jie;LIU Jianghao(School of Mechanical and Electrical Engineering,Xi'an Polytechnic University,Xi'an,Shaanxi 710600,China;Shaanxi Key Laboratory of Functional Garment Fabrics,Xi'an Polytechnic University,Xi'an,Shaanxi 710600,China)

机构地区：[1]西安工程大学机电工程学院,陕西西安710600 [2]西安工程大学陕西省功能性服装面料重点实验室,陕西西安710600

出　　处：《纺织学报》2024年第4期96-104,共9页Journal of Textile Research

基　　金：国家自然科学基金青年基金项目(61701384);陕西省教育厅重点科学研究计划项目(20JS051);西安工程大学柯桥纺织产业创新研究院项目(19RQYB03);陕西省自然科学基础研究计划(2023-JC-YB-288);湖北省数字化纺织装备重点实验室开放课题项目(KDTL2020005)。

摘　　要：为提高织物疵点检测算法对简单纹理织物、模式纹理织物及条纹纹理织物检测时的普适性和准确性,提出了一种基于多尺度Laws纹理能量和低秩分解的织物疵点检测算法。首先,对织物图像进行直方图均衡化并将处理后的图像均匀划分为子图像块;其次,对每个子图像块提取28个纹理能量特征(利用7个Laws滤波模板在4个尺度上提取),计算所有子图像块提取到的特征均值并组成特征矩阵;然后,利用特征矩阵构建低秩分解模型,通过方向交替方法(ADM)优化求解,得到低秩部分和稀疏部分;最后,由稀疏部分生成疵点显著图,采用迭代阈值分割法对其进行分割,得到织物疵点检测结果。为验证该算法的有效性,在织物图像数据集选取了包含错纬、断经、跳花、破洞等常见疵点的织物图像,并将实验结果与已有3种算法进行了对比。实验结果表明,该算法在简单纹理织物、模式纹理织物及条纹纹理织物检测上具有更好的普适性和准确性,且检测效率具有一定优势。Objective In order to improve the universality and accuracy of fabric defects detection algorithm for simple textured fabric,pattern textured fabric and stripe textured fabric.A fabric defects detection algorithm based on multiscale Laws of texture energy and low-rank decomposition was proposed.Method Firstly,the fabric image is equalized by histogram,and the image is evenly divided into sub-image blocks.Secondly,28 texture energy features were extracted from each sub-image block(7 Laws filter templates were used to extract the features on 4 scales),and the mean values of all sub-image blocks were calculated,and the feature matrix was formed.Then,the low-rank decomposition model is constructed by the feature matrix,and the low-rank and spare parts are obtained by the direction alternation method(ADM).Finally,the defect saliency maps are generated from the sparse part,which is segmented by iterative threshold segmentation method,and the fabric defect detection results are obtained.Results To validate the effectiveness of the proposed algorithm,the ZJU-Leaper colored fabric dataset is used for experiments.Three images,including simple textured fabric,patterned textured fabric,and striped textured fabric,were selected for the experiment,including common defects such as wrong weft,broken warp,flaking and holes.The image size is 512 pixels×512 pixels.First,the key elements of the algorithm are analyzed.In the feature extraction section,the saliency maps generated with different numbers of Laws filter templates are compared.In the low-rank decomposition part,the saliency maps generated by choosing different balance factors are compared.The experimental results show that 28 Laws filter templates have the best detection effect,and the fabric defect saliency maps is the best when λ values of simple texture,pattern texture and stripe texture fabric are 0.02,0.12 and 0.05,respectively.Secondly,the defect saliency maps generated by the proposed algorithm in this paper is compared with Gabor combined with low-rank decomposi

关 键 词：织物疵点 疵点检测 Laws纹理 纹理能量 特征提取 矩阵低秩分解 

分 类 号：TS101[轻工技术与工程—纺织工程] TP391[轻工技术与工程—纺织科学与工程]