## 1 Introduction
Our method addresses variability directly through architecture, without relying solely on artificial augmentation. However, most of these methods rely heavily on clean training data or extensive data augmentation strategies, and they tend to degrade under real-world noisy conditions. [PERSON] et al. [7] used haptic sensors on a robotic arm to automate and standardize data collection and create fabric datasets. In addition, to detect fabric types, an integrated CNN with an attention mechanism in the channel domain has been developed. This method improves data collection and introduces attention, which is aligned with our use of attention modules. However, we extend this concept by incorporating logarithmic shrinkage to enhance noise robustness. [PERSON] et al. [8] proposed a semi-supervised structure error detection method that is pixel-by-pixel combined with a multitasking (MT) averaging tutorial. To improve defect segmentation, a multitasking detection model was created by utilizing its supplementary information for fault area detection, fault contour detection, and fault distance map detection. While multitasking can boost performance by learning auxiliary tasks, it increases model complexity. Our model remains efficient while achieving robustness through its shrinkage mechanism. [PERSON] et al. [9] used and tested several deep-learning models for unsupervised defect detection, including image preprocessing and CNNs. Furthermore, multispectral imaging, combining normal red-green-blue (RGB) and near-infrared (NIR) imaging, was implemented to improve and increase the accuracy of our system. The proposed method by EfficientNet CNN provided high accuracy, reaching approximately 99%. Multispectral imaging improves detection but adds hardware complexity. Our work maintains high accuracy using only standard RGB images, which is more practical for real-world deployment. In another paper, an improved YOLOv4 algorithm for fabric defect detection with higher accuracy is proposed, employing a new structure based on SoftPool rather than MaxPool [10]. Experiment results show that, when compared to the original YOLOV4, the enhanced YOLOV4 increases mean average precision (mAP) by 6% while decreasing frame per second (FPS) by only 2%. Despite the promising accuracy values, these approaches often depend on complex architectures, additional modalities (e.g., multispectral or tactile data), or heavy computational resources, which limits their applicability in real-time or cost-sensitive industrial environments. In another article, an improved YOLOV4 algorithm for fabric defect detection with higher accuracy is proposed, employing a new SPP structure based on SoftPool rather than MaxPool [11]. Experiment results show that, when compared to the original YOLOV4, the enhanced YOLOV4 increases mAP by 6% while decreasing FPS by only 2%. SoftPool enhances feature selection, but these models in [10; 11] still operate under ideal imaging conditions. In contrast, our Log-DRSN explicitly targets noisy environments. [PERSON] et al. [12] proposed an unsupervised and real-time two-stage framework for fabric defect detection. In the first stage, density maps were generated to identify abnormal regions, and in the second stage, defects were automatically classified based on these maps. [PERSON] et al. [13] enhanced the YOLOv5 network with an attention mechanism to improve accuracy and efficiency in fabric defect detection. The model was strengthened with a coordinate attention mechanism to more precisely localize defects. Their study achieved 96.9% accuracy and a 95.3% F1-score on a publicly available fabric defect dataset. The average maximum success rate of 87.94% was attained in [14], where spectral domain and spatial domain techniques were employed for fabric detection and classification. Statistical, morphological, fast Fourier transform (FFT), discrete cosine transforms (DCT), and Gabor and Wavelet transforms are among the methods employed.