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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摘要

针对含有复杂图案的纺织品瑕疵检测问题,提出基于畸变校正与视觉显著特征的纺织品瑕疵检测方法.首先,计算图像周期,获取最佳分块模板,根据模板对图像进行畸变校正.再对图像进行卡通纹理层分解,只保留具有图像主要特征的卡通层.然后,采用改进的上下文视觉显著性算法提取卡通层的显著性特征,分离具有高显著性特征的瑕疵与低显著性特征的背景.最后,采用K-means聚类算法突出显著图中的瑕疵,完成瑕疵检测.实验表明,文中方法对星型、箱型和点型图案纺织品的瑕疵检测的平均查全率较高,同时平均查全-查准效果也较优.

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	龙涵彬
	狄岚
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关键词 ：畸变校正, 卡通纹理层分解, 视觉显著特征, K-means聚类, 瑕疵检测

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

收稿日期: 2020-08-24

基金资助:

2020年度道路交通安全公安部重点实验室开放课题(No.2020ZDSYSKFKT03-2)资助

通讯作者: 狄岚,硕士,副教授,主要研究方向为模式识别、数字图像处理.E-mail:dilan@jiangnan.edu.cn.

作者简介: 龙涵彬,硕士研究生,主要研究方向为计算机视觉.E-mail:6191611032@stu.jiangnan.edu.cn;梁久祯,博士,教授,主要研究方向为计算机视觉.E-mail:jzliang@cczu.edu.cn.

引用本文:

龙涵彬, 狄岚, 梁久祯. 基于畸变校正与视觉显著特征的纺织品瑕疵检测[J]. 模式识别与人工智能, 2020, 33(12): 1122-1134. LONG Hanbin, DI Lan, LIANG Jiuzhen. Fabric Defect Detection Based on Distortion Correction and Visual Salient Features. , 2020, 33(12): 1122-1134.

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

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