基于畸变校正与视觉显著特征的纺织品瑕疵检测

doi:10.16451/j.cnki.issn1003-6059.202012007中图法分类号TP181

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Abstract

Aiming at fabric defect detection with complex patterns,a fabric defect detection method based on distortion correction and visual salient features is proposed.Firstly,the image period is calculated to obtain the best block template,and then the image distortion is corrected according to the template.Secondly,the image is decomposed into texture layer and cartoon layer,and only the cartoon layer with the main features of the image is retained.Then,the improved context-aware saliency algorithm is applied to obtain the saliency feature of the image cartoon layer,so that the defects with high saliency features are separated from the background with low saliency features.Finally,the K-means clustering algorithm is utilized to highlight defects and complete defect detection.Experiments show that the proposed method achieves a high average recall rate for star,box and dot pattern fabrics,and the average recall precision effect of the proposed method is superior to that of the existing methods.

Key words： Distortion Correction Texture Cartoon Layer Decomposition Visual Salient Feature K-means Clustering Defect Detection

Received: 24 August 2020

Fund:

Open Project of Key Laboratory of Ministry of Pub-lic Security for Road Traffic Safety(No.2020ZDSYSKFKT03-2)

Corresponding Authors: DILan,master,associateprofessor.Herresearchinterestsincludepatternrecognitionanddigitalimageprocessing.

About author:: LONG Hanbin,master student.His research interests include computer vision;LIANG Jiuzhen,Ph.D.,professor.His research interests include computer vision.

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	LONG Hanbin
	DI Lan
	LIANG Jiuzhen

Cite this article:

LONG Hanbin,DI Lan,LIANG Jiuzhen. Fabric Defect Detection Based on Distortion Correction and Visual Salient Features[J]. , 2020, 33(12): 1122-1134.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202012007中图法分类号TP181 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I12/1122

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