Fabric defect detection by Fourier analysis
Fabric defect detection by Fourier analysis
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DOI：
10.1109/ias.1999.805975
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发表时间：
1999-10
期刊：
影响因子：
--
通讯作者：
Chi-Ho Chan;G. Pang
中科院分区：
文献类型：
--
作者：
Chi-Ho Chan;G. Pang
许多织物瑕疵很小，无法区分，仅通过监测强度变化很难检测到这些瑕疵。无瑕疵织物是一种重复的、规则的全局纹理，傅立叶变换可以用来监测织物的空间频谱。当织物出现瑕疵时，它的规则结构会发生变化，从而使频谱中某些特定位置的相应强度发生变化。然而，三维频谱分析是非常困难的。本文利用一个模拟的织物模型来理解织物结构在图像空间和频率空间的关系。在三维频谱的基础上，定义了两个有意义的频谱图，并用于分析织物的瑕疵。这两张图被称为中心空间频谱。纱疵大致可分为四类：(1)双纱瑕疵；(2)纱线缺失；(3)纤网或破布；及(4)纱线密度变化。在利用模拟模型和实际样本对这四类缺陷进行评价后，提取了中心空间频谱的7个特征参数用于缺陷分类。
Many fabric defects are very small and undistinguishable, which are very difficult to detect by only monitoring the intensity change. Faultless fabric is a repetitive and regular global texture and Fourier transform can be applied to monitor the spatial frequency spectrum of a fabric. When a defect occurs in fabric, its regular structure is changed so that the corresponding intensity at some specific positions of the frequency spectrum would change. However, the three-dimensional frequency spectrum is very difficult to analyze. In this paper, a simulated fabric model is used to understand the relationship between the fabric structure in the image space and in the frequency space. Based on the three-dimensional frequency spectrum, two significant spectrum diagrams are defined and used for analyzing the fabric defect. These two diagrams are called the central spatial frequency spectrums. The defects are broadly classified into four classes: (1) double yarn; (2) missing yarn; (3) webs or broken fabric; and (4) yarn densities variation. After evaluating these four classes of defects using some simulated models and real samples, seven characteristic parameters for central spatial frequency spectrum are extracted for defect classification.