## 1 Introduction
Although comprehensive in features, this approach does not leverage deep learning, limiting its adaptability. Our deep model learns features end-to-end without handcrafted transformations. With the VGG19 architecture, it has been utilized to identify fabric flaws [15]. The success rate achieved is 94.65%. This method uses a standard CNN architecture without specific modifications for defect detection or noise resilience. Our work introduces architectural changes (logarithmic shrinkage and attention) to address these issues. The success rates for detecting fabric flaws using the deep transfer learning approach were 95.53% and 93.82%, respectively, in Ref. [16], where InceptionV3 and DenseNet121 were utilized. These are powerful models but trained in a transfer learning setting that may not adapt well to fabric-specific noise. Our model is trained from scratch with fabric defect noise in mind. By using simple image processing approaches, [PERSON] et al. [17] were able to detect and categorize fabric problems with a 95% success rate, which represents an improvement of 50% over manual defect detection. Although showing improvement over manual inspection, this method is outdated compared to modern deep learning techniques. Our method automates detection with state-of-the-art performance. To address these limitations, our proposed Logarithmic Deep Residual Shrinkage Network (Log-DRSN) incorporates a logarithmic mapping combined with residual shrinkage and attention mechanisms. This design improves robustness against noise without requiring additional sensor modalities or sacrificing computational efficiency, making it more suitable for practical textile inspection environments. Table 1 presents a comparative analysis of existing studies in the literature regarding fabric defect detection and classification. In this study, a new deep learning-based model is created for categorizing fabric defects. We preferred to apply the RSN model to this problem thanks to the flexibility of the RSN model and the customizability of the RSN structure and parameters. In addition, the RSN architecture was chosen thanks to the advantages of having the ability to recognize and classify complex faults and the ability to detect the difference between normal and abnormal situations. The suggested deep attention-based logarithmic deep residual shrinkage network (Log-DRSN) model and the classical residual shrinkage network (RSN) model both use all the layers developed for the RSN model. For both models, all layers and parameters are identical. To experimentally test the suggested model, we produced our own dataset. In our dataset, there are four main kinds of fabric failures. Despite promising results, existing defect detection approaches often suffer from one or more limitations such as sensitivity to noise, reliance on handcrafted features, or poor generalizability across fabric types. These challenges significantly affect performance in practical industrial environments. To address these gaps, this study proposes a novel Logarithmic Deep Residual Shrinkage Network (Log-DRSN), which combines logarithmic transformation with adaptive shrinkage and attention mechanisms. This architecture aims to provide robustness against noise and improved generalization without the need for additional data modalities or extensive feature engineering. The following are some of this study's advantages and contributions:  
* The proposed Log-DRSN model significantly improves classification accuracy on fabric defect detection, achieving up to 99.17% accuracy on noiseless data and demonstrating robust performance even with noisy inputs. * The integration of a novel deep attention mechanism and logarithmic activation enhances feature learning, noise robustness, and model interpretability, while also reducing computational complexity compared to traditional activation functions. * Extensive experimental validation confirms that the model offers superior performance and numerical stability, making it a promising tool for predictive maintenance in the textile industry.