基于模板校正与低秩分解的纺织品瑕疵检测方法

doi:10.16451/j.cnki.issn1003-6059.201903008

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Abstract To solve the problem of tensile deformation of periodic fabric, a fabric defect detection method based on template correction and low-rank decomposition is proposed. Firstly, the original image is corrected by the template to reduce the influence of stretching deformation on the detection results. Then, a low-rank correction decomposition model is proposed including a low-rank term, sparse term and correction term. The model can be solved by the alternating direction method to generate a low-rank matrix and a sparse matrix. Finally, the optimal threshold segmentation algorithm is utilized to segment the significant images generated by the sparse matrix. Experiments on standard databases show that the recall rate of the proposed method is improved.

Key words： Fabric with Periodic Pattern Template Correction Low-Rank Decomposition Defect Detection

Received: 23 October 2018

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China( No.61170121)

Corresponding Authors: LIANG Jiuzhen, Ph.D., professor. His research interests include computer vision.

About author:: (JI Xuan, master student. His research interests include computer vision.)
(HOU Zhenjie, Ph.D., professor. His research interests include computer vision.)
(CHANG Xingzhi, Ph.D., associate senior engineer. His research interests include computer vision.)
(LIU Wei, master student. His research interests include computer vision.)

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	JI Xuan
	LIANG Jiuzhen
	HOU Zhenjie
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	LIU Wei

Cite this article:

JI Xuan,LIANG Jiuzhen,HOU Zhenjie等. Fabric Defect Detection Based on Template Correction and Low-Rank Decomposition[J]. , 2019, 32(3): 268-277.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201903008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I3/268

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