  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.
