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.

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。