Deep learning for quality control in woven fabrics

Abstract

Automated defect detection in fabrics is a key challenge in quality control within the textile industry. This study proposes a deep learning-based methodology to identify defects in Batavia and Sarga fabrics. In the first stage, an autoencoder was used to filter anomalous images, enabling the creation of a dataset with sufficient defective cases, which are otherwise difficult to obtain in textile production. Subsequently, convolutional neural networks (DenseNet121, EfficientNetB0/B3, Xception, and VGG) were trained using data augmentation techniques and stratified cross-validation. For Batavia fabrics, DenseNet121 achieved an AU-ROC of 0.88 and an AU-PR of 0.93, demonstrating high detection capability. For Sarga fabrics, three different references (42402, 45433, and 43105) were considered, showing more variable performance across models and datasets. Nonetheless, models such as ResNet101 and Xception achieved competitive results. The results indicate that the combination of autoencoder and CNN facilitates the generation of balanced datasets and enables consistent defect detection, although performance depends on the type of fabric and the specific reference, suggesting that model selection should be adapted to the characteristics of each case.