## 1 Introduction  
Defects in the fabric rolls that machines produce are very typical in the textile industry. All diagnostic applications used in the industry are included in the quality control system. Quality control is crucial for lowering costs and enhancing the final product. Maintaining the quality control process manually takes time and is ineffective because everyone doing the control identifies the errors based on their personal experiences. Additionally, it is improbable that the investigator will be able to find all the flaws because of the possibility that they will miss some of them. Quality control should be carried out automatically for the reasons listed above as well as the fact that quality is crucial to the textile industry. There are numerous studies in the literature to diagnose fabric faults. A technique to find fabric faults that could happen during fabric manufacturing on knitting machines was developed by [PERSON] et al. [1]. In the dataset, they used 5923 defective images and 3242 poor ones. Filtering, feature extraction, and classification using artificial neural network (ANN) were the processes used. The performance of fabric fault classification was 90% [1]. However, their approach relied heavily on manual feature extraction and lacks adaptability to complex or noisy environments, limiting real-world applicability. In contrast, our method performs automatic deep feature learning and is designed to be robust against noise. A method for the automatic localization and classification of flaws in yarn-dyed fabrics was reported by [PERSON] et al. [2] and is based on YOLOV2. First, 276 images of defects in yarn-dyed fabrics were gathered and prepared before labeling. While effective for localization, YOLOv2 is primarily designed for general object detection and may not sufficiently capture subtle texture-based defects in fabrics. Our approach focuses on enhancing sensitivity to fine-grained defect patterns. A quicker multi-channel RCNN is another tool used in the detection of fabric defects [3]. To detect fabric defects, geometric data augmentation and generative adversarial network (GAN)-based data augmentation were employed to supplement the training examples in [3]. The proposed method significantly outperforms the traditional fast- region convolutional neural network (Fast-RCNN) in simulation. With the Fast-RCNN augmented dataset, the recommended multi-channel average accuracy has increased to 90.05%. Although data augmentation techniques help expand training data, and they do not inherently address robustness to real-world noise. Unlike this work, our model integrates noise robustness directly into its architecture. [PERSON] et al. [4] classified fabric defects using artificial neural networks. The accuracy rate was determined to be 99%. Fabric defects were classified using U-Net, a convolution neural network (CNN) approach. Similarly to [1], this method uses shallow architectures that may not generalize well across varying fabric types or noisy backgrounds. Our model leverages a deeper structure with residual and attention mechanisms to overcome such limitations. In another study, they proposed a deep architecture for detecting fabric defects in real time during production [5]. They were 99% accurate. Although U-Net offers strong segmentation performance, it is computationally intensive and primarily suited for pixel-wise segmentation. Our model, on the other hand, offers a classification solution that balances accuracy and computational efficiency. [PERSON] et al. [6] proposed defect transfer, a highly contentious data augmentation method. The location and size of a defect in network training did not fully depend on the background texture because the defect could be anywhere on a background texture of any size. With only 1% fabric defect data in the ZJU-Leaper dataset, the method achieved comparable performance to the most recent fabric defect classification methods. This approach introduces novelty in data augmentation but still depends on synthetic data distribution.