基于模板校正与低秩分解的纺织品瑕疵检测方法

针对周期性纺织品存在的拉伸变形问题,提出结合模板校正与低秩分解的纺织品瑕疵检测方法.首先对原图像进行模板校正,减少图像拉伸变形对检测结果的影响.然后提出低秩校正分解模型,包含低秩项、稀疏项和校正项,通过交替方向法优化求解,生成低秩矩阵和稀疏矩阵.最后利用最优阈值分割算法,分割由稀疏矩阵产生的显著图,完成瑕疵检测.在标准数据库上的实验表明,文中方法的查全率有所提高.

Fabric Defect Detection Based on Template Correction and Low-Rank Decomposition

To solve the problem of tensile deformation of periodic fabric, a fabric defect detection method based on template correction and low-rank decomposition is proposed. Firstly, the original image is corrected by the template to reduce the influence of stretching deformation on the detection results. Then, a low-rank correction decomposition model is proposed including a low-rank term, sparse term and correction term. The model can be solved by the alternating direction method to generate a low-rank matrix and a sparse matrix. Finally, the optimal threshold segmentation algorithm is utilized to segment the significant images generated by the sparse matrix. Experiments on standard databases show that the recall rate of the proposed method is improved.