# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 1 Introduction  
Fabric defect detection stands as a pivotal component within textile quality control processes. Manual inspection methods fall short of meeting the demands of actual production. The integration of machine vision technology has emerged as a promising avenue, offering high-precision automatic inspection capabilities to supplant manual scrutiny. This transition holds substantial significance for enhancing textile production quality and efficiency [1]. In the realm of machine vision technology, the efficiency of fabric defect detection algorithms plays a decisive role in enabling automated detection. Current fabric defect detection methodologies can be broadly categorized into three types [2]. Firstly, frequency domain algorithms, such as wavelet transform [3; 4; 5] and Gabor filtering [6; 7], leverage Fourier transformations [8] of images to extract spectral features for defect identification, primarily effective for simple background features, like solid-color fabrics. However, their efficacy diminishes when detecting defects in fabrics with intricate textures. Secondly,model-based algorithms, including Gaussian mixture modeling [9; 10], utilize autoregressive modeling to extract error regions in fabric images. While effective for simpler textures, these methods often falter in detecting defects within more complex fabric textures. Lastly, spatial domain-based methods like grayscale symbiotic matrix algorithms [11; 12] attempt to discern normal texture from defects by learning texture features. However, their extensive computational demands and lack of robustness hinder practical application. Despite significant progress in traditional fabric defect detection algorithms, limitations persist in terms of detection accuracy, generalization, and real-time performance. These shortcomings impede their practical deployment in production settings. In recent years, the evolution of deep learning has propelled convolutional neural networks (CNNs) to remarkable heights in the domain of image recognition, fostering their swift integration across diverse industries. Within the sphere of defect detection, CNNs manifest through two predominant avenues: target detection algorithms and semantic segmentation. Defect detection based on target detection algorithms unfolds along two principal trajectories, characterized by Regional Convolutional Neural Networks [13] (R-CNN) and models rooted in You Only Look Once [14] (YOLO) architectures. R-CNN, initially designed for specific target determination, has found application in fabric defect detection, notably through improved iterations like Faster R-CNN [15]. Notably, [PERSON] et al. [16] introduced the Faster GG R-CNN model, employing a two-stage training approach that integrates Gabor filters into the Faster R-CNN framework via genetic algorithms and backpropagation. This innovation effectively addresses the challenge posed by complex background textures in fabric defect detection. In a parallel development, [PERSON] et al. [17] enhanced YOLOv5 using attention mechanisms to augment defect-related features. They proposed a lightweight fabric defect detection method exhibiting promising results when tested on fabric lebmish images. Despite the expediency and high accuracy of target detection-based defect detection methods, their efficacy diminishes when faced with fabrics adorned with intricate patterns and when training samples are scarce. The reliance of these methods on the volume of training samples poses a significant limitation, especially given the challenges associated with collecting defect samples. In scenarios where the number of training samples is insufficient, these methods may encounter limitations and potential failures. The defect detection approach based on image segmentation involves dividing an image into distinct regions and extracting areas of interest. In this method, the fabric image to be inspected is reconstructed using a pre-trained reconstruction model, resulting in a reconstructed image.