Defect Detection and Classification for Plain Woven Fabric Based on Deep Learning

被引：8

作者：

Guan, Miao ^{[1
]}

Zhong, Zhaozhun ^{[1
]}

Rui, Yannian ^{[2
]}

Zheng, Hongjing ^{[3
]}

Wu, Xiongjun ^{[4
]}

机构：

[1] Soochow Univ, Sch Iron & Steel, Suzhou, Peoples R China

[2] Soochow Univ, Coll Text & Clothing Engn, Suzhou, Peoples R China

[3] Soochow Vocat Univ, Sch Comp Engn, Suzhou, Peoples R China

[4] China Aerosp Sci & Technol Corp, Acad 8, Shanghai Acad Space Flight Technol, Inst 802, Shanghai, Peoples R China

来源：

2019 SEVENTH INTERNATIONAL CONFERENCE ON ADVANCED CLOUD AND BIG DATA (CBD) | 2019年

关键词：

defect detection; plain woven fabric; deep learning; convolutional neural network; VGG model;

D O I：

10.1109/CBD.2019.00060

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Defect detection for plain woven fabric is investigated in this paper. The original RGB images are converted into gray scale images and further enhanced by gray level adjustment. Several different filtering methods are used to denoise for comparison and ideal low pass filter is selected for the filtering of adjusted gray scale images. Deep learning based on convolutional neural network is suggested for the detection and classification of the filtered images using the VGG model. Experiment results verified the effectiveness of the proposed method.

引用

页码：297 / 302

页数：6

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