基于稀疏字典优选的织物疵点检测方法  

Fabric defect detection method using optimized sparse dictionary

作　　者：王小虎 潘如如[1] 高卫东[1] 周建[1] WANG Xiaohu;PAN Ruru;GAO Weidong;ZHOU Jian(Key Laboratory of Eco-Textiles(Jiangnan University),Wuxi,Jiangsu 214122,China)

机构地区：[1]生态纺织教育部重点实验室(江南大学),江苏无锡214122

出　　处：《纺织学报》2023年第8期81-87,共7页Journal of Textile Research

基　　金：国家自然科学基金项目(61501209)。

摘　　要：针对稀疏字典算法检测速度慢,无法满足实时检测需求的问题,提出了一种基于稀疏字典优化的疵点检测算法。首先采用一定尺寸的窗口对正常样本滑动取块进行学习得到字典库;然后对字典库进行分组优选,其策略是依据样本被近似的程度,按顺序分组挑选最优字典组;之后检测时选用字典组对织物图像求解系数并进行重构,得到重构图像及相应的残差图像,最后对残差图像进行疵点区域的判定。实验结果表明,此方法检测准确率平均可达96.22%,检出率高于无约束字典学习方法,图像大小为512像素×512像素时平均每张用时208 ms,为稀疏字典方法的0.26%,可达到在保证检测精度的同时仍具有实时性的效果。Objective Textile fabric defects are generally caused by raw materials(warp or weft yarns),mechanical failures and human factors in the production process,and they seriously impair the quality of final products.At present,most of the defect inspection is conducted by human inspectors,resulting in low efficiency and high laboring cost.Therefore,it is of great significance to apply fast and reliable image processing and machine vision techniques to perform automated defect detection instead of human.Method Sparse dictionary learning method has excellent adaptability in representing complex fabrics textures.However,the learning and solving of sparse dictionary take a long time,making it hard to meet the real time requirements in the industrial scenarios.This work proposed a novel dictionary grouping strategy to speed up the sparse coding process in detection stage while guaranteeing the detection accuracy.Firstly,the sliding patch scheme was adopted to learn dictionary from normal fabric samples.Secondly,the learned dictionary was optimized by dividing into groups,and such strategy is to select several optimal dictionary groups with respect to the degree of approximation.Next,the optimized dictionary groups were used to reconstruct a test image to obtain its residual image.As the final step,the reconstructed errors were applied to identify defect areas from normal ones.Results To compare the computation time of different algorithms,only the total running time in detection stage was calculated,not including the dictionary learning or dictionary grouping.The experimental results showed that the sparse dictionary algorithm took a longest running time among them,the proposed algorithm took the second longest time,and the unconstrained dictionary used the shortest time(Tabl.1).The reason that the proposed algorithm was able to reduce most of the time is that the entire algorithm process advances the process of finding the optimal dictionary atoms for the sparse dictionary(sparse coding)and limits the number of dictionary

关 键 词：稀疏表达 字典优化 织物疵点 实时检测 图像处理 

分 类 号：TS111[轻工技术与工程—纺织材料与纺织品设计]