计算机应用 ›› 2014, Vol. 34 ›› Issue (10): 3009-3013.DOI: 10.11772/j.issn.1001-9081.2014.10.3009

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于稀疏编码的双尺度布匹瑕疵检测

张龙剑1,张卓2,范赐恩1,邓德祥1   

  1. 1. 武汉大学 电子信息学院，武汉 430072；
    2. 上海航天电子通讯设备研究所，上海 201100
  • 收稿日期:2014-04-28 修回日期:2014-06-23 发布日期:2014-10-30 出版日期:2014-10-01
  • 通讯作者: 张龙剑
  • 基金资助:

    国家自然科学基金面上项目;CAST创新基金资助项目

Dual-scale fabric defect detection based on sparse coding

ZHANG Longjian1,ZHANG Zhuo2,FAN Ci'en1,DENG Dexiang1   

  1. 1. School of Electronic Information, Wuhan University, Wuhan Hubei 430072, China;
    2. Shanghai Institute of Aerospace Electronic Communications Equipment, Shanghai 201100, China
  • Received:2014-04-28 Revised:2014-06-23 Online:2014-10-30 Published:2014-10-01
  • Contact: ZHANG Longjian
  • Supported by:

    ;General Project Fund

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摘要：

瑕疵检测是布匹质量控制的重要环节。为了使检测算法具有较高的通用性和检测精度，提出了一种基于稀疏编码的双尺度布匹瑕疵检测算法，综合了大尺度下检测稳定性高和小尺度下检测敏感性高的优点。首先，采用一种小规模过完备字典的训练方法得到大小尺度下的字典；其次，利用检测图像块在字典上的投影提取检测特征；最后，利用距离融合方法综合大小尺度下的检测结果。小规模完备字典的采用以及对大尺度下的检测进行下采样，克服了因引入双尺度而造成计算量太大的缺点。实验采用德国TILDA布匹样本库，实验结果表明，该算法能有效地检测平纹布、格子布、条纹布上的瑕疵，综合检测率达到95.9%，并且计算量适中，能够满足工业实时检测的要求，具有实际应用的价值。

Abstract:

Defect detection is an important part of fabric quality control. To make the detection algorithm possess good commonality and high detection accuracy, a dual-scale fabric defect detection algorithm based on sparse coding was proposed. The algorithm combined the advantage of high stability under large-scale and the advantage of high detection sensitivity under small-scale. At first, the dictionaries under large and small scales were obtained through a small-scale over-complete dictionary training method. Then, the projection of detection image block on the over-complete dictionary was used to extract detection characteristics. Finally, the detection results under dual-scale were fused by the means of distance fusion. The algorithm overcame the disadvantage of large computation because of the introduction of dual-scale while using small-scale over-complete dictionary and downsampling the detection image under large-scale. TILDA Textile Texture Data base was used in the experiment. The experimental results show that the algorithm can effectively detect defects on plain, gingham and striped fabric, the comprehensive detection rate achieves 95.9%. And its moderate amount of calculation can satisfy the requirement of industrial real-time detection, so it does have much value of practical application.

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