# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 1 Introduction
This approach demonstrates potential in amplifying model training efficiency and mitigating the reliance on extensive data during training. Presently, migration learning methods for blemish detection encompass two primary categories: pre-trained model methods and domain adaptive methods. Pre-trained model approaches [31; 32] often overlook the intrinsic similarity between source and target domain data, leading the target network to acquire task-irrelevant features, thereby impeding the effectiveness of migration. Conversely, domain adaptive methods facilitate the transfer of pertinent features learned by the source domain model to the target task by identifying shared features or similarities between the domains [33]. Early domain adaptive techniques predominantly leveraged shallow machine learning methodologies, such as Support Vector Machines (SVM) [34] and Maximum Mean Discrepancy (MMD) [35], aiming to mitigate distributional disparities between source and target domains by altering feature spaces or re-weighting source domain data. Nevertheless, these methods exhibited limitations in handling high-dimensional data and complex tasks. As research evolved, methodologies centered on feature selection and transformation emerged to better accommodate diverse domain data. These encompassed techniques like feature selection [36] and principal component analysis (PCA) [37] aimed at reducing feature space dimensionality or extracting discriminative features. Advancements in the field witnessed a fusion of deep neural networks with domain-adaptive tasks. [PERSON] et al. [38] proposed a domain-adaptive DAYOLOv5 model specifically tailored for surface defect detection in tiles, exhibiting commendable performance even with limited datasets. Similarly, [PERSON] et al. [39] introduced a fabric defect detection algorithm founded on two-stage deep migration learning, successfully transferring defect features from solid-color cotton linen to a checkered fabric detection model. This migration encapsulated defect size features and notably reduced the requisite training samples. Despite the strides made by these migration methods in enhancing detection performance and model training efficiency, challenges persist, particularly in achieving high detection accuracy when confronted with intricately patterned fabric defects. This research considers blemishes as distortions to the original fabric, where regardless of their specific type, the texture within the blemished region diverges from the original fabric texture. To address the challenges posed by limited sample sizes and intricate patterns affecting blemish reconstruction accuracy, a novel fabric blemish detection algorithm combining the SA-Pix2 pix network and migration learning is proposed. The primary objective is the restoration of defective regions to their pristine state. Subsequently, dissimilarity computations between the reconstructed and defective images are performed, followed by image segmentation to facilitate defect detection. To mitigate the poor reconstruction accuracy attributed to the Pix2 pix network generator's limited sensory field within convolutional layers and its inadequacy in establishing long-range dependencies within images, the concept of image self-attention is integrated into the network architecture. This innovation leads to the formulation of the SA-Pix2 pix network, effectively remedying the network's inability to extract comprehensive global features from images, thus enhancing reconstruction accuracy. Moreover, recognizing the inefficacy of the adversarial loss function within generative adversarial neural networks for processing fabric images with complex pattern textures, we introduce an improved structural loss function. This, combined with the original loss function, forms the target loss function, aimed at alleviating the network's struggles with intricate fabric textures.