基于稀疏优化的织物缺陷检测方法

针对传统织物检测算法存在严重的误检、漏检现象且微小缺陷不易检测等问题,提出一种基于稀疏优化的织物缺陷检测方法。对织物图像进行预处理,加强图像的对比度;将一些无缺陷织物样本图像分块,采用K-means算法将图像块聚类成簇,每个类簇训练一个子字典,选择合适的子字典并利用优化的稀疏表示模型对待测图像进行重构;最后生成残差图像,利用最大熵阈值法对残差图像进行分割,从而检测出织物的疵点。实验结果表明:该方法可以有效检测织物的各种缺陷以及微小缺陷,与其他算法相比,该算法也具有较高的检测精度。

Fabric Defect Detection Method Based on Sparse Optimization

Because the traditional fabric detection algorithm has serious false detection and miss detection, and it is difficult to detect small defects, a fabric defect detection method based on sparse optimization was proposed. The fabric image was preprocessed to enhance the contrast of the image.Some non defective fabric sample images were segmented, and the image patches were clustered into clusters by using K-means algorithm. A sub dictionary was trained for each cluster and the image was reconstructed by using the optimized sparse representation model with the appropriate sub dictionary. Finally, the residual image was generated, and the residual image was segmented by using the maximum entropy threshold method to detect the defect of the fabric. The experimental results show that the algorithm can be used to effectively detect various defects and minor defects of fabrics. Compared with other algorithms, the algorithm also has high detection accuracy.