# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 4 Discussion  
This paper proposes a novel blemish detection methodology leveraging an SA-Pix2 pix network combined with migration learning. This approach synthesizes the strengths of GAN networks, self-attention mechanisms, and migration learning to notably enhance the reconstruction accuracy of blemish images, especially when working with limited samples. Addressing the limitations of generative adversarial neural networks in processing image \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **ReNet-D** & **SDDM-PS** & **SA-Pix2 pix** \\ \hline Time (ms) & 38.25 & 43.82 & 46.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of processing time. Figure 13: Comparison of experimental results of missing warp pointsby each method. (**a**) Defect sample; (**b**) detection result of ReNet-D; (**c**) detection result of SDDM-PS; (**d**) detection result of SA-Pix2 pix. details, we introduce the L1 loss function and an enhanced structural loss function to form a comprehensive target loss function, augmenting the CGAN network's capacity to handle image intricacies. Through comparative analysis between networks employing and omitting the self-attention mechanism and distinct loss functions, our findings verify that the self-attention mechanism significantly amplifies the network's global feature extraction capabilities. Its introduction enables the network to reconstruct defects based on global image features, markedly improving reconstruction accuracy. Conversely, networks lacking the self-attention mechanism rely solely on local features for defect reconstruction. Consequently, they may fail to reconstruct intermediate regions, leading to errors in the final detection results. Moreover, our experiments confirm that integrating both the L1 loss function and the improved SSIM loss function notably enhances the network's ability to process intricate fabric images with complex pattern textures. However, employing either loss function in isolation or without introducing any additional loss function diminishes the network's defect detection accuracy. Comparative experimental studies highlight the superior performance of our method over ReNet-D and SDDM-PS methodologies in enhancing both the completeness of defective regions and the precision of their localization, particularly in scenarios with limited sample sizes. While the incorporation of the self-attention mechanism and improved loss functions into the Pix2 pix network elevates its image reconstruction capabilities, further enhancements are needed to improve detection accuracy for abrasions and missing warp defects. While this paper successfully achieves defect detection through image reconstruction, there exist certain limitations in the obtained results, leading to errors in defect detection in some images. To further enhance defect detection accuracy, additional research avenues warrant exploration in the following areas:  
Firstly, investigating the implementation of deformable convolutions instead of conventional convolutions for image sampling presents a promising direction. Deformable convolutions introduce positional offset parameters to each sampling point, allowing the convolution to autonomously learn sampling point selection within the network. This transformation alters the receptive field from rectangular to polygonal, enabling better fitting to object contours with multi-layer polygonal receptive fields. This enhancement could significantly aid in learning fabric pattern features, potentially playing a pivotal role in image reconstruction. However, leveraging deformable convolutions might present challenges in model training and convergence. Subsequent research could focus on optimizing the integration of deformable convolutions within the model to improve image reconstruction accuracy and consequently enhance defect detection performance. Secondly, augmenting the network by integrating Transformer models, renowned for their capacity to directly focus on global image features, holds promise.