论文标题
织物缺陷检测的一级模型
One-Class Model for Fabric Defect Detection
论文作者
论文摘要
自动化和准确的织物缺陷检查系统的需求很高，是纺织工业中缓慢，不一致，容易出错且昂贵的人类操作员的替代。以前的努力集中在某些类型的织物或缺陷上，这不是理想的解决方案。在本文中，我们提出了一种新型的单级模型，该模型能够检测到不同织物类型的各种缺陷。我们的模型利用了精心设计的Gabor滤清器库来分析织物纹理。然后，我们利用AutoCoder的先进深度学习算法从Gabor Filter Bank的输出中学习一般功能表示形式。最后，我们开发了一个最近的邻居密度估计器来定位潜在的缺陷并将其绘制在织物图像上。我们通过对诸如普通，图案和旋转的织物等各种织物进行测试，证明了所提出的模型的有效性和鲁棒性。我们的模型还基于标准的织物缺陷词汇表在我们的数据集上实现了0.895的真实正率（又称召回）值。
An automated and accurate fabric defect inspection system is in high demand as a replacement for slow, inconsistent, error-prone, and expensive human operators in the textile industry. Previous efforts focused on certain types of fabrics or defects, which is not an ideal solution. In this paper, we propose a novel one-class model that is capable of detecting various defects on different fabric types. Our model takes advantage of a well-designed Gabor filter bank to analyze fabric texture. We then leverage an advanced deep learning algorithm, autoencoder, to learn general feature representations from the outputs of the Gabor filter bank. Lastly, we develop a nearest neighbor density estimator to locate potential defects and draw them on the fabric images. We demonstrate the effectiveness and robustness of the proposed model by testing it on various types of fabrics such as plain, patterned, and rotated fabrics. Our model also achieves a true positive rate (a.k.a recall) value of 0.895 with no false alarms on our dataset based upon the Standard Fabric Defect Glossary.
