多纹理分级融合的织物缺陷检测算法

doi:10.13475/j.fzxb.20180704708

纺织学报 ›› 2019, Vol. 40 ›› Issue (06): 117-124.doi: 10.13475/j.fzxb.20180704708

多纹理分级融合的织物缺陷检测算法

朱浩¹, 丁辉¹^,²^,³, 尚媛园¹^,²^,³, 邵珠宏¹^,²^,⁴

1.首都师范大学信息工程学院, 北京 100048
2.高可靠嵌入式系统技术北京市工程技术研究中心,北京 100048
3.北京成像理论与技术高精尖创新中心, 北京 100048
4.北京数学与信息交叉科学协同创新中心, 北京 100048

收稿日期:2018-07-19 修回日期:2019-02-26 出版日期:2019-06-15 发布日期:2019-06-25
通讯作者: 丁辉
基金资助:
国家自然科学基金项目(61876112, 61303104, 61601311, 61603022);北京市属高校高水平教师队伍建设支持计划项目(CIT&TCD20170322);北京市教委科研计划项目(SQKM201810028018);首都师范大学创新团队项目(PXM19530050151)

Defect detection algorithm for multiple texture hierarchical fusion fabric

ZHU Hao¹, DING Hui¹^,²^,³, SHANG Yuanyuan¹^,²^,³, SHAO Zhuhong¹^,²^,⁴

1. College of Information Engineering, Capital Normal University, Beijing 100048, China
2. Beijing Engineering Research Center of High Reliable Embedded System, Beijing 100048, China
3. Beijing Advanced Innovation Center for Imaging Theory and Technology, Beijing 100048, China
4. Collaborative Innovation Center for Mathematics and Information of Beijing, Beijing 100048, China

Received:2018-07-19 Revised:2019-02-26 Online:2019-06-15 Published:2019-06-25
Contact: DING Hui

摘要/Abstract

摘要：

针对织物缺陷检测过程中纹理分布的复杂多样性引起误检和漏检的问题,结合织物纹理周期性特点,提出一种多纹理分级融合的织物缺陷检测算法。在检测过程中,首先利用织物缺陷图像的Tamura粗糙度图,对缺陷区域进行初步定位和自适应性生长,将初步定位的区域映射到原始织物图像中;其次根据织物图像的周期性分布特征,对初步定位区域进行分块,提取图像块的局部相位量化(LPQ)特征、Tamura特征,并将2种特征融合;然后计算融合特征与正常块特征的相似度,获取相似度图;最后将初步定位区域的经纬向特征图与相似度特征图融合,检测缺陷存在的区域。经TILDA织物纹理库数据的实验测试结果表明,缺陷区域的初步定位和自适应生长,降低了缺陷检测过程的冗余度,提高了检测效率,避免了织物缺陷检测过程中的误检和漏检情况。

关键词: 织物缺陷检测, 织物纹理, 特征融合, Tamura特征, 局部相位量化特征

Abstract: Aim

ing at the false detection and missing detection caused by complexity and diversity of texture distribution in fabric defect detection, considering the periodicity of fabric texture, an algorithm of multi-texture gradation fusion for fabric defect detection was proposed. In the process of testing, firstly, the defect region was subjected to primary positioning and self-adaptive growth by using the Tamura roughness graph of the fabric defect image, then the primary positioned region was mapped to the original fabric image. The primary positioned region was blocked and the local phase quantization (LPQ) texture feature and Tamura texture feature of each image block were extracted, and the two different texture features were fused. The similarity between the fusion feature and the normal block feature was calculated to obtain the similarity image. Finally, the longitude and latitude feature map and the similarity feature map were fused to find the region of the defects in fabric images. The experimental results on TILDA dataset show that the new approach can reduce the redundancy of defect detection and improve the detection efficiency, and can avoid errors and omissions in the process of defect detection.

Key words: defect detection, fabric texture, feature fusion, Tamura feature, local phase quantization feature

中图分类号:

TP399

朱浩, 丁辉, 尚媛园, 邵珠宏. 多纹理分级融合的织物缺陷检测算法[J]. 纺织学报, 2019, 40(06): 117-124.

ZHU Hao, DING Hui, SHANG Yuanyuan, SHAO Zhuhong. Defect detection algorithm for multiple texture hierarchical fusion fabric[J]. Journal of Textile Research, 2019, 40(06): 117-124.

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织物编号	LBP	LPQ	经纬向	Tamura	LBP+经纬向	本文
试样1^#	14.96	30.97	6.69	7.91	19.81	13.65
试样2^#	14.73	31.12	6.72	7.89	19.93	13.15
试样3^#	14.60	31.04	6.71	7.92	19.94	12.95
试样4^#	15.10	31.26	6.72	7.90	19.74	13.28

织物编号	LBP算法		LPQ算法		经纬向算法		LBP+经纬向算法		本文算法
织物编号	检出率	误检率	检出率	误检率	检出率	误检率	检出率	误检率	检出率	误检率
试样1^#	84.72	15.27	85.64	14.35	81.94	18.05	91.66	8.33	93.05	6.94
试样2^#	83.33	16.66	83.79	16.20	80.55	19.44	94.44	5.55	93.05	6.94
试样3^#	61.11	38.88	63.88	36.11	43.05	56.94	58.33	41.66	65.27	34.72
试样4^#	54.16	45.83	56.01	43.98	41.66	58.33	69.44	30.55	72.77	27.77

多纹理分级融合的织物缺陷检测算法

Defect detection algorithm for multiple texture hierarchical fusion fabric

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