Fabric Defect Detection Algorithm for Dense Road and Sparse Road

被引：1

作者：

Li, Dejun ^{[1
]}

Fang, Han ^{[1
]}

Zheng, Liwen ^{[1
]}

Ji, Changjun ^{[1
]}

Yuan, Haoran ^{[1
]}

机构：

[1] Wuhan Text Univ Elect & Elect Engn, Wuhan 430200, Hubei, Peoples R China

来源：

2018 4TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND MATERIAL APPLICATION | 2019年 / 252卷

关键词：

D O I：

10.1088/1755-1315/252/2/022077

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In order to improve the results of fabric defect detection with less obvious features such as dense road and sparse road, a Gaussian hybrid clustering algorithm is proposed. Firstly, the image is preprocessed by means of mean filter, and then a Gabor filter and Gaussian mixture clustering algorithm are used to identify the defects of the image to be detected. The experimental results show that compared with other defect detection methods, the method is effective in detecting the defects of fabrics such as dense road and sparse fabric, and has some practical value in defect detection.

引用

页数：7

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