针对当前自动织物检测方法大多仍需人工挑选训练集而无法实现无监督学习的问题,提出使用中值稳健扩展局部二值模式(MRELBP)特征的无疵图像筛选方法与欠完备字典重构疵点检测方法,实现自动无监督疵点检测,并采用自适应字典大小搜索算法,自动选取合适字典大小.算法首先对织物样本进行无疵图像的自动筛选,然后使用K-SVD算法将筛选后的正常图像块作为训练集获取欠完备字典,最后将通过计算重构后的结构相似性指标(SSIM)作为阈值进行疵点检测.在334张含有经向、纬向、块状疵点的平纹白坯布上进行实验,与使用残差分割疵点的K-SVD方法相比,正检率平均提升21.81%,误检率平均降低0.72%,每张图像的检测速率平均提升50%,在AITEX数据集上取得了平均83.29%的正检率,证明了本算法的有效性.

To address the issue that most current automatic fabric defect detection methods still require manual selection of training sets and cannot achieve unsupervised learning, an automatic unsupervised defect detection method using the median robust extended local binary pattern (MRELBP) feature for flawless image screening and an under-complete dictionary reconstruction method for defect point detection are proposed. An adaptive dictionary size search algorithm is also proposed to automatically select a suitable dictionary size. First, the algorithm selects the flawless images from fabric images. Then, K-SVD is applied to obtain an under-complete dictionary from the normal image blocks as the training set. Finally, the reconstruction-base scheme is used to identify defects with the structural similarity index measure (SSIM) threshold. Experimental results of 334 plain fabrics with warp, weft, and block defects show that compared to the K-SVD method that uses residual segmentation for defect detection, the proposed method increases the correct detection rate up to 21.81%, reduces the false detection rate up to 0.72%, and a 50% increase in detection speed per image on average. The proposed algorithm achieved an average correct detection rate of 83.29% on the AITEX dataset, demonstrating its effectiveness.