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Defect detection and Identification in Textile Fabrics using Multi Resolution Combined Statistical and spatial Frequency Method

机译:使用多分辨率组合统计和空间频率法在纺织织物中缺陷检测和识别

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In textile industry, reliable and accurate quality control and inspection becomes an important element. Presently, this is still accomplished by human experience, which is more time consuming and is also prone to errors. Hence automated visual inspection systems become mandatory in textile industries. This Paper presents a novel algorithm of fabric defect detection by making use of Multi Resolution Combined Statistical and Spatial Frequency Method. Defect detection consists of two phases, first is the training and next is the testing phase. In the training phase, the reference fabric images are cropped into non-overlapping sub-windows. By applying MRCSF the features of the textile fabrics are extracted and stored in the database. During the testing phase the same procedure is applied for test fabric and the features are compared with database information. Based on the comparison results, each sub-window is categorized as defective or non-defective. The classification rate obtained by the process of simulation using MATLAB was found to be 99%.
机译:在纺织工业中,可靠和准确的质量控制和检查成为一个重要的元素。目前,这仍然是通过人类经验完成的,这更耗时,并且也容易出错。因此,自动视觉检测系统在纺织工业中成为强制性。本文通过利用多分辨率组合统计和空间频率方法提出了一种新颖的织物缺陷检测算法。缺陷检测包括两个阶段,首先是培训,接下来是测试阶段。在训练阶段,参考结构图像被裁剪到非重叠的子窗口中。通过应用MRCSF,提取纺织面料的特征并将其存储在数据库中。在测试阶段期间,应用相同的过程用于测试结构,并且将特征与数据库信息进行比较。基于比较结果,每个子窗口被分类为有缺陷或不缺陷。发现使用MATLAB的模拟过程获得的分类率为99%。

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