Real-Time Vision-Based System for Textile Fabric Inspection

This paper presents an automatic vision-based system for quality control of web textile fabrics. The general hardware and software platform developed to solve this problem is presented and a powerful algorithm for defect inspection is proposed. Based on the improved binary, textural and neural network algorithms the proposed method gives good results in the detection of many types of fabric defects under real industrial conditions, where the presence of many types of noise is an inevitable phenomenon. A high detection rate with good localization accuracy, low rate of false alarms, compatibility with standard inspection tools and low price are the main advantages of the proposed system as well as the overall inspection approach.     

基于改进Gabor优化选择的布匹瑕疵检测方法

针对传统Gabor优化选择方法用于布匹瑕疵检测时准确率低、鲁棒性差的缺点,提出了改进的优化选择方法,通过瑕疵图像与标准图像Gabor滤波后分块子图均值差平方和的代价函数实现优化选择。设置一组不同方向和尺度的Gabor滤波器并提取标准图像滤波后相关参数,通过改进的优化选择方法实现滤波后瑕疵图像的最优选择,利用迭代式阈值分割对最优滤波后图像进行二值分割,根据分割后图像的像素信息检测是否含有瑕疵并输出瑕疵信息。实验验证该方法,并与传统优化选择方法对比分析,结果表明该方法运算量较少,且检查性能高,可满足在线检测要求。     

基于最优Gabor滤波器的织物缺陷检测

织物缺陷的自动检测是纺织行业所面临的技术难题之一。为了对织物缺陷进行快速准确的自动检测，建立了一种基于Gabor滤波器的织物缺陷自动检测方法，即首先根据无缺陷织物的结构特征，应用小生境遗传算法寻找最优的Gabor滤波器参数和分割阈值；然后将待检图像通过滤波和分割来得到检测结果，同时根据响应矩阵的极小值点来计算缺陷的形状特征。该方法应用于帘子布的缺陷检测的实验结果证明，该方法是可行的和有效的，而且还具有适用性广、识别能力强、检测速度快等特点。     

Neural network based detection of local textile defects

A new approach for the segmentation of local textile defects using feed-forward neural network is presented. Every fabric defect alters the gray-level arrangement of neighboring pixels, and this change is used to segment the defects. The feature vector for every pixel is extracted from the gray-level arrangement of its neighboring pixels. Principal component analysis using singular value decomposition is used to reduce the dimension of feature vectors. Experimental results using this approach illustrate a high degree of robustness for the detection of a variety of fabric defects. The acceptance of a visual inspection system depends on economical aspects as well. Therefore, a new low-cost solution for the fast web inspection using linear neural network is also presented. The experimental results obtained from the real fabric defects, for the two approaches proposed in this paper, have confirmed their usefulness.     

用于织物疵点检测的最优Gabor滤波器设计

提出一种基于织物纹理特征的最优Gabor滤波器设计方法．分别建立了正常纹理匹配和疵点纹理匹配的Gabor滤波器优化设计模型，并采用小生境遗传算法对两种模型进行求解．通过比较和分析两种滤波器的检测结果发现，由正常纹理匹配模型得到的最优Gabor滤波器更适宜于织物疵点的识别与分割，并且其中心频率与纹理图像功率谱中能量最集中的谐波成分相一致，因而可以极大地缩短求解优化模型所花费的时间．     

Fast defect detection in homogeneous flat surface products

This paper introduces a novel hybrid approach for both defect detection and localization in homogeneous flat surface products. Real time defect detection in industrial products is a challenging problem. Fast production speeds and the variable nature of production defects complicate the process of automating the defect detection task. Speeding up the detection process is achieved in this paper by implementing a hybrid approach that is based on the statistical decision theory, multi-scale and multi-directional analysis and a neural network implementation of the optimal Bayesian classifier. The coefficient of variation is first used as a homogeneity measure for approximate defect localization. Second, features are extracted from the log Gabor filter bank response to accurately localize and detect the defect while reducing the complexity of Gabor based inspection approaches. A probabilistic neural network (PNN) is used for fast defect classification based on the maximum posterior probability of the Log-Gabor based statistical features. Experimental results show a major performance enhancement over existing defect detection approaches.     

Fabric defect detection using local contrast deviations

Defect inspection is a vital step for quality assurance in fabric production. The development of a fully automated fabric defect detection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to delicate features of defects complicated by variations in weave textures and changes in environmental factors (e.g., illumination, noise, etc.). Based on characteristics of fabric structure, an approach of using local contrast deviation (LCD) is proposed for fabric defect detection in this paper. LCD is a parameter used to describe features of the contrast difference in four directions between the analyzed image and a defect-free image of the same fabric, and is used with a bilevel threshold function for defect segmentation. The validation tests on the developed algorithms were performed with fabric images from TILDA’s Textile Texture Database and captured by a line-scan camera on an inspection machine. The experimental results show that the proposed method has robustness and simplicity as opposed to the approach of using modified local binary patterns (LBP).     

基于Gabor滤波器组的织物疵点检测方法

给出了基于Gabor滤波器组的织物疵点检测方法。在分析Gabor滤波器时频特性的基础上,针对素色坯布织物疵点图像,设计了椭圆形多尺度多方向的Gabor滤波器组,并应用该滤波器组在频域对织物疵点图像进行滤波处理,对滤波后的多幅图像进行融合与分割处理,将疵点从织物背景中分割出来,从而实现了疵点的检测。实验结果证明了该方法的有效性。     

Automatic visual inspection: An approach with multi-instance learning

One of the industrial applications of computer vision is automatic visual inspection. In the last decade, standard supervised learning methods have been used to detect defects in different kind of products. These methods are trained with a set of images where every image has to be manually segmented and labeled by experts in the application domain. These manual segmentations require a large amount of high quality delineations (on pixels), which can be time consuming and often a difficult task. Multi-instance learning (MIL), in contrast to standard supervised classifiers, avoids this task and can, therefore, be trained with weakly labeled images. In this paper, we propose an approach for the automatic visual inspection that uses MIL for defect detection. The approach has been tested with data from three artificial benchmark datasets and three real-world industrial scenarios: inspection of artificial teeth, weld defect detection and fishbone detection. Results show that the proposed approach can be used with weakly labeled images for defect detection on automatic visual inspection systems. This approach is able to increase the area under the receiver-operating characteristic curve (AUC) up to 6.3% compared with the naïve MIL approach of propagating the bag labels.     

A statistical approach to the inspection checklist formal synthesisand improvement

Proposes a statistical approach to the formal synthesis and improvement of inspection checklists. The approach is based on defect causal analysis and defect modeling. The defect model is developed using IBM's Orthogonal Defect Classification. A case study describes the steps required and a tool for the implementation. The advantages and disadvantages of both empirical and statistical methods are discussed and compared. It is suggested that a statistical approach should be used in conjunction with the empirical approach. The main advantage of the proposed technique is that it allows us to tune a checklist according to the most recent project experience and to identify optimal checklist items even when a source document does not exist     

A novel multiscale-multidirectional autocorrelation approach for defect detection in homogeneous flat surfaces

Defect detection in flat web surface products is a challenging task. Reliable vision-based systems for detection of defects require the suitable selection of a huge set of parameters which highly impact the performance of these systems such as image resolution/scale, size of the scanning window, feature extraction, direction of scanning, classifier type and parameters and system performance evaluation measures. This paper addresses these issues and introduces a novel multi-scale and multi-directional (MSMD) autocorrelation function (ACF)-based approach for reliable defect detection and localization in homogeneous web surfaces. The proposed approach has been experimentally tested on samples from the well-known TILDA textiles database and wallboards. Performance evaluation using the system Precision, Recall (Sensitivity), Specificity, Accuracy, Youden’s index, F-measure and Matthews correlation coefficient has shown that the MSMD ACF approach outperforms the state-of-the-art approaches like MSMD Log-Gabor filters. The MSMD ACFs approach results in better performance indicators for defect detection than the Log-Gabor based approach in addition to being about 2–6 times faster in defect detection.     

基于模板匹配单色布匹瑕疵检测算法

提出一种基于模板匹配的单色布匹瑕疵检测算法,该检测算法首先存储待测布匹的无瑕疵模板图,并对模板图进行分块,然后对待测样本图进行相同的分块操作,进一步利用模板匹配方法对相同分块区域的样本图与模板图进行匹配查找,得到最优匹配图像.在匹配过程中,对模板图按照一定比例进行扩充,以提高匹配的准确性.最后将样本图与最优匹配图像进行差值对比实现布匹瑕疵检测.实验结果表明,算法弥补了传统Gabor算法高度依赖纹理的缺陷,提高了对于纹理模糊的单色布匹瑕疵检测准确率,检测效率与精度满足验布现场需求.     

随机纹理表面缺陷检测方法与应用

针对随机纹理表面缺陷检测问题,提出了一种基于Gabor小波的检测方法。该方法首先利用实值2维 Gabor小波对图像进行多通道滤波;然后通过对滤波图像进行非线性处理和平滑滤波产生通道能量图像（特征图像）;接着在学习阶段估计学习样本（不含缺陷）特征的统计参数,并用于指导检测阶段特征图像的阈值化;最后在不同尺度和方向,对阈值化后的特征图像进行融合,并二值化,以达到减小虚警率的目的。实验结果表明,该方法检测效果好,且要求学习样本少,适用于不同缺陷类型和各种检测问题。     

一种故障检测滤波器的多目标优化设计方法

提出一种基于观测器的故障检测滤波器的非凸多目标优化设计方法.针对线性时不变动态系统,构建一个由输出观测器和后滤波器组成的故障检测滤波器,将其残差动态特性描述为非凸的双线性矩阵不等式形式.利用双线性矩阵不等式中可完全平方非正定项的上界替代原非正定项,将双线性矩阵不等式转化为一组线性矩阵不等式,进而获得多目标优化问题的可解条件以及观测器增益与后滤波器矩阵的求解方法.仿真算例说明了算法的有效性.     

一种新的Gabor优化选择检测算法及应用

针对基于Gabor小波的布匹瑕疵检测运算量大,准确率较低等问题,提出了一种新的优化选择算法,即均值归一化方差算法。布匹瑕疵图像通过多通道Gabor小波;分别计算各通道滤波图像的均值和方差;计算方差与均值的比值,选择比值最大的通道作为优化选择通道。考虑到布匹瑕疵主要表现为经向与纬向瑕疵,因此基于该算法设计了1×2二维Gabor小波布匹瑕疵自动检测系统,降低了运算量,提高了准确率,达到了93%。     

Investigating the defect detection effectiveness and cost benefit of nominal inspection teams

Inspection is an effective but also expensive quality assurance activity to find defects early during software development. The defect detection process, team size, and staff hours invested can have a considerable impact on the defect detection effectiveness and cost-benefit of an inspection. In this paper, we use empirical data and a probabilistic model to estimate this impact for nominal (noncommunicating) inspection teams in an experiment context. Further, the analysis investigates how cutting off the inspection after a certain time frame would influence inspection performance. Main findings of the investigation are: 1) Using combinations of different reading techniques in a team is considerably more effective than using the best single technique only (regardless of the observed level of effort). 2) For optimizing the inspection performance, determining the optimal process mix in a team is more important than adding an inspector (above a certain team size) in our model. 3) A high level of defect detection effectiveness is much more costly to achieve than a moderate level since the average cost for the defects found by the inspector last added to a team increases more than linearly with growing effort investment. The work provides an initial baseline of inspection performance with regard to process diversity and effort in inspection teams. We encourage further studies on the topic of time usage with defect detection techniques and its effect on inspection effectiveness in a variety of inspection contexts to support inspection planning with limited resources.     

基于匹配Gabor滤波器的规则纹理缺陷检测方法

许多工业产品表面纹理都可以被认为是由基本纹理单元在空间按照一定的规则进行排列组合的结果,但由于各种原因,这些有规则纹理图象经常出现的一些缺陷,因而检测这些有规则纹理图象的缺陷是机器视觉检测的重要内容,为了对这种缺陷进行有效地检测,在对这类纹理图象进行功率谱分析的基础上,根据人眼的视觉原理,设计了两类匹配Gabor滤波器,即正常纹理匹配Gabor滤波器和缺陷纹理匹配Gabor滤波器,前者能够突出正常纹理,抑制缺陷纹理,而后者恰恰相反,在将这两类滤波器用于规则纹理图象缺陷的自动检测时,均获得了良好的检测精度和速度。     

Optimal non-periodic inspection scheme for a multi-component repairable system using A1 search algorithm

This paper proposes an approach for finding an optimal non-periodic inspection scheme on a finite time horizon for a multi-component repairable system. The system consists of several components, each of which is subjected to soft failure. Soft failures of each component do not cause the system to stop functioning, but increase the system operating costs and are detected only if inspection is performed. Thus, the system is inspected at the scheduled inspection instances and if any of its components is found to have failed, the failed component is minimally repaired. The system’s expected total cost associated with a given inspection scheme includes inspection costs, repair costs, and the penalty costs that are incurred due to the time delay between the actual occurrence of a soft failure of the components and its detection at an inspection. The objective is to determine the optimal inspection scheme which minimizes system’s expected total cost.In the proposed approach, the system’s expected total cost is first formulated in terms of inspection scheme. Then, A¹ search algorithm, with a proposed heuristic cost function for calculating lower bounds, is employed to search through alternative inspection schemes to determine the optimal one. The proposed approach is illustrated through a numerical example.     

A new strategy for defect inspection by the virtual inspection in semiconductor wafer fabrication

The defect of process equipments is a major factor that impairs the yields in the mass production of semiconductor wafer fabrication and it is a main supervision means to use high-resolution defect inspection tools to detect and monitor the defect damage. Due to the high investment costs of these inspection tools and the resulting decrease in the throughput, how to improve the sampling rate is an important issue for the associated inspection strategy. This paper proposes a new concept and implementation of virtual inspection (VI) to enhance the detection and monitoring of defect in semiconductor production process. The underlying theory of the VI concept is that the state variables identifications (SVIDs) of process equipments can reflect the process quality effectively and loyally. The approach of VI is to combine the application of the fault detection and classification (FDC), and the defect library and the re-engineering of inspection procedure to reach the full-scope of strategic objective. VI enables the defect monitoring to enter a new era by promoting the monitoring level of defect inspection from the previous lot-sampling basis to the wafer-sampling level, and hence upgrades the sampling strategy from random-sampling to full and right-sampling. In this study, various typical defect cases are utilized to illustrate how to create VI models and verify the reliability of the proposed approach. Furthermore, a feasible architecture of the VI implementation for mass production in semiconductor factory is presented in the paper.     

基于Gabor滤波器组的实时疵点图像分割

使用Gabor滤波器组进行布匹在线疵点检测与疵点图像分割。通过定义一个分辨力函数和一些合成的疵点图像,对已有的Gabor滤波器组的参数选择方式做出评价,提出了在实时应用场合有效地确定Gabor滤波器组参数的方法。分析指出：Gabor滤波器的实部输出是主要因素;滤波器的方位角仅选取疵点出现得最多的水平和垂直方向,而径向中心频率的选取依赖于纹理本身的固有频率;滤波器的长度也应与纹理的固有周期一致。尽管Gabor滤波器的个数减少到4个以满足实时性要求,但结果表明,滤波器组仍能很好地检测和分割出大多数疵点。