An End-to-end Deep Learning Framework for Automated Woven Fabric Pattern Recognition using UNet, GAN, and CNN

The quality and production of high-grade textiles largely depend on accurate recognition of weave patterns, which are traditionally identified through manual visual inspection. However, this approach is subjective, time-consuming, and error-prone. While machine learning methods offer automation, they often rely on handcrafted features sensitive to lighting and imaging variations, limiting their robustness and scalability. Even deep learning models face generalization issues due to domain shifts in real-world acquisition conditions, and to address these challenges, a novel deep learning framework that combines a Convolutional Neural Network (CNN) with Generative Adversarial Networks (GANs) for end-to-end fabric classification is proposed. The approach integrates geometric and photometric data augmentation with UNet-based image denoising, while the GAN component generates high-quality synthetic images to enhance training diversity and feature learning. Experiments on a woven fabric dataset demonstrate that this method achieves state-of-the-art performance, with a balanced accuracy of 99.1%, outperforming baseline models in accuracy, generalizability, and robustness to visual distortions. This framework offers a scalable and reliable solution for automated textile inspection, with significant implications for improving efficiency and reducing manual labor in industrial fabric manufacturing. This work integrates CNN, GAN, and U-shaped Convolutional Neural Network (UNet)modules into a single optimization-based learning pipeline rather than handling denoising, data creation, and classification as distinct processes. The joint training mechanism creates a feedback loop between the generative and discriminative networks, going a step further methodologically than standard fine-tuning practices.