Abstract

For assuring the quality of woven fabric, use of machine vision system in the weaving industry has become an essential demand. In this paper, we present a novel approach for detecting defects on woven homogeneous/inhomogeneous fabrics by generating 3D fabric image containing information of the entire range of inhomogeneity of the fabric class, followed by the designing of a fabric class specific optimized 3D cylindrical band-reject filter and defect specific energy threshold value. The method was validated on 10 types of fabric defects taken place on 582 defective and 79 non defective fabric images from TILDA database. Detection success rate of 97.5%, false positive rate of 1.8%, recall value of 96.77% and precision of 98.17% was achieved during detection of defects by this method. The method showed promising results during detection of defects from Salt & Pepper and Gaussian noise corrupted fabric images.

摘要

为了保证机织物的质量，在织造行业使用机器视觉系统已成为必不可少的需求。在本文中，我们提出了一种检测编织同质/非同质织物缺陷的新方法，通过生成包含织物类别整个不均匀范围信息的 3D 织物图像，然后设计织物类别特定的优化 3D 圆柱带 -拒绝过滤器和缺陷特定能量阈值。该方法在来自 TILDA 数据库的 582 个有缺陷和 79 个无缺陷织物图像上发生的 10 种织物缺陷上进行了验证。该方法在缺陷检测过程中实现了97.5%的检测成功率、1.8%的误报率、96.77%的召回率和98.17%的准确率。