One-Class Model for Fabric Defect Detection
One-Class Model for Fabric Defect Detection
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DOI：
10.5121/csit.2021.112314
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Haotian Zhou;Yixin Chen;David Troendle;Byunghyun Jang
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文献类型：
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作者：
Haotian Zhou;Yixin Chen;David Troendle;Byunghyun Jang
在纺织工业中，自动化和精确的织物缺陷检测系统作为缓慢、不一致、容易出错和昂贵的人工操作员的替代品有着很高的需求。以前的努力集中在某些类型的织物或缺陷上，这不是一个理想的解决方案。在本文中，我们提出了一种新的单类模型，能够检测不同织物类型上的各种缺陷。我们的模型利用设计良好的Gabor滤波器组来分析织物纹理。然后，我们利用先进的深度学习算法，自动编码器，从Gabor滤波器组的输出中学习一般特征表示。最后，我们开发了一个最近邻密度估计器来定位潜在的缺陷并在织物图像上绘制它们。我们通过对各种类型的织物（如平纹，图案和旋转织物）进行测试来证明所提出模型的有效性和鲁棒性。在基于标准织物缺陷词汇表的数据集上，我们的模型还实现了0.895的真阳性率（也称为召回）值，没有假警报。
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.