One-Class Model for Fabric Defect Detection
This addresses the need for reliable, automated inspection in the textile industry to replace human operators, though it appears incremental as it builds on existing methods like Gabor filters and autoencoders.
The paper tackles automated fabric defect detection by proposing a one-class model that uses Gabor filters and autoencoders to identify various defects across different fabric types, achieving a true positive rate of 0.895 with no false alarms on their dataset.
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.