| 刘秀平,冯国栋,袁 皓,王柯欣,闫焕营.结构化矩阵分解的网状织物缺陷检测方法[J].电子测量与仪器学报,2022,36(10):181-188 |
| 结构化矩阵分解的网状织物缺陷检测方法 |
| Defect detection method of agricultural mesh fabric based onstructured matrix decomposition |
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| DOI： |
| 中文关键词: 显著性 缺陷检测 图像处理 特征融合 |
| 英文关键词:significance defect detection image processing feature fusion |
| 基金项目:西安市科技局项目(GXYD75)、陕西省科技厅项目(2018GY 173)资助 |
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| 中文摘要: |
| 针对在网状织物缺陷检测过程中因纹理复杂造成误检问题,提出了一种结构化矩阵分解的网状织物缺陷检测方法。
首先,通过 Retinex 算法对图像进行增强,利用所提取的底层图像特征生成特征矩阵,并将其分解为含有织物图像背景信息
的低秩矩阵和含有缺陷信息的稀疏矩阵;其次,引入了高级先验矩阵和索引树两个部分,通过利用增强后图像进行获取,并
对两个部分进行特征融合,实现缺陷显著性增强。 通过计算稀疏矩阵的值,获得缺陷的显著性的大小;最后,通过最佳阈值
分割算法分割缺陷显著图,从而得到缺陷检测结果。 利用公开数据集 TILDA 和 BASLER 工业相机采集到的网状织物缺陷图
像验证了算法的性能。 研究表明,与其他算法相比,本文算法的识别准确率达到 94. 25%,召回率达到 92. 48%,分类准确率
达到 90. 12%。 |
| 英文摘要: |
| Aiming at the problem of misdetection caused by complex texture during the defect detection process of mesh fabric, a
structured matrix decomposition method for mesh fabric defect detection is proposed. First, the image is enhanced by the Retinex
algorithm, the feature matrix is generated using the extracted underlying image features, and it is decomposed into a low-rank matrix
containing fabric image background information and a sparse matrix containing defect information. Secondly, the enhanced image is used
to obtain Advanced priori matrix and index tree to achieve significant enhancement of defects. By calculating the value of the sparse
matrix, the saliency of the defect is obtained. Finally, the defect saliency map is segmented by the optimal threshold segmentation
algorithm to obtain the defect detection result. The performance of the algorithm is verified by using the defect images of the mesh fabric
collected by the public data set TILDA and the CCD industrial camera. The results show that compared with other algorithms, the
recognition accuracy of this algorithm reaches 94. 25%, the recall rate reaches 92. 48%, and the classification accuracy rate
reaches 90. 12%. |
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