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

Summary

An end-to-end deep learning framework integrating CNNs, GANs, and UNet-based denoising was developed for automated recognition of woven fabric patterns. Augmentation and synthetic image generation improved robustness. The method achieved 99.1% accuracy, providing a scalable solution for industrial textile inspection.

Abstract

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.

Introduction

Design, redesign, textural analysis, and the aesthetic appearance of materials are all greatly influenced by pattern, which is the most important factor in the production of cloth. Fabric is a historical human invention that has evolved from handcrafted cloth to modern machine-produced digital fabrics¹. Before being further processed by weaving machinery, woven cloth patterns must be identified. Nowadays, traditional methods that rely on visual perception, enhanced by tools such as microscopes or magnifying glasses, remain the mainstay of cloth pattern detection. Usually, a specialist with the necessary training and experience performs this man....

Protocol

This study did not involve human participants or animal subjects. Therefore, ethical approval and informed consent were not required. The proposed end-to-end woven fabric classification framework (Figure 1) consists of four sequential stages: image acquisition, preprocessing and augmentation, model training, and evaluation. The output of each stage serves as the input to the next.

Stage 1: Image acquisition: Woven fabric images were obtained from the dataset and captured using a digital camera under a controlled illumination setup. Images were acquired at 300 dpi using a 50 mm fixed focal lengt....

Results

Evaluation Metrics
Accuracy is the most widely used evaluation statistic for categorization. This is defined as the proportion of accurate predictions among all predictions. Equation 11 shows that balanced accuracy is the appropriate performance evaluation metric to use in these circumstances¹⁸. "N" represents the number of classes. TP, TN, FP, and FN denote the numbers of true positives, true negatives, false positives, and false negatives, respectively, in t.......

Discussion

While the proposed GAN–CNN framework demonstrates strong generalization, there are other alternative methods such as transformer-based vision architectures, attention-enhanced CNNs, or self-supervised representation learning that could address similar hypotheses. These may offer advantages in feature interpretability or computational efficiency and should be evaluated comparatively in the future^17,19.

The improved recognition and.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Canon EOS 90D	Canon Inc., Tokyo, Japan	EOS 90D 32.5 MP CMOS sensor	Digital imaging camera for fabric image acquisition
Intel i7 Processor	Intel Corporation, Santa Clara, CA, USA	https://www.intel.com	Multi-core central processing unit (CPU) for computational processing
Keras Deep Learning Framework	Google LLC, Mountain View, CA, USA	https://keras.io	Deep learning software library for neural network development
LED Ring Light (5500 K)	Commercially available laboratory illumination system	Not applicable	Controlled illumination source for uniform image acquisition
NVIDIA GeForce GTX1060MQ GPU	NVIDIA Corporation, Santa Clara, CA, USA	https://www.nvidia.com	Graphics processing unit (GPU) for accelerated deep learning computation
OpenCV Library	Open Source Community	https://opencv.org	Computer vision software library for image processing
Python Programming Language	Python Software Foundation, Wilmington, DE, USA	https://www.python.org	High-level programming language for machine learning implementation
ResNet50 Pretrained Model	Microsoft Research / He et al. (2016)	https://keras.io/api/applications/resnet/	Pretrained convolutional neural network architecture for feature extraction and classification
scikit-learn Library	Open Source Community	https://scikit-learn.org	Machine learning software library for data analysis and model evaluation
TensorFlow Framework	Google LLC, Mountain View, CA, USA	https://www.tensorflow.org	Deep learning framework for neural network training and deployment
Warp Yarn (Polyester, 83 dtex)	In-house Textile Laboratory	Not applicable	Synthetic textile yarn used as warp material in woven fabric samples
Woven Fabric Samples (Plain, Satin, Twill)	In-house Textile Laboratory	Polyester yarns 110 dtex	Representative woven textile samples with different weave structures