## 1 Introduction
### Related Work  
#### 1.2.1 Methods Based on 2D Images  
Traditional methods for surface defect detection on magnetic tiles, particularly those based on 2D images, have evolved considerably. [PERSON] et al. [13] utilized a wavelet transform to detect low-contrast defects under varying lighting conditions, while [PERSON] et al. [14] employed a discrete shearlet transform to enhance contrast between defective and normal regions, followed by a two-step defect extraction strategy. [PERSON] et al. [15] applied Gabor functions to represent surface textures and refined the Gabor energy spectrum for fuzzy C-means clustering, enabling defect identification based on regional grayscale variations. Despite their advancements, traditional methods struggle in complex scenarios, such as low contrast or diverse defect shapes. With the rise of deep learning, more sophisticated methods have emerged, leveraging neural networks to automatically learn task-specific features directly from raw data, enhancing defect detection efficiency and adaptability. [PERSON] et al. [16] introduced semantic-guided and edge-aware modules to enhance spatial details and improve defect boundary clarity. [PERSON] et al. [17] combined a densely nested U-Net with a residual shape-adaptive module to extract multi-level features, ensuring stable training and supporting lightweight model pruning for tiny defect detection. Similarly, [PERSON] et al. [18] proposed a U-shaped multi-view residual attention network with Gaussian residual attention convolution to handle low-contrast features, while [PERSON] et al. [19] utilized a low-rank decomposition and the Retinex theory to enhance defect contrast. [PERSON] et al. [20; 21] integrated a U-Net with decision networks and saliency preprocessing, improving defect localization and computational efficiency. Other approaches, like [PERSON] et al. 's [22] multi-layer perceptron for low-contrast images and [PERSON] et al. 's [23] rotation-invariant convolutions, further contribute to more accurate and efficient defect classification. As manufacturing processes improve, large defects on product surfaces have become rarer, while tiny defects, often invisible to conventional cameras, remain challenging to detect. This has led to class imbalances between defective and non-defective samples, making dataset creation both time-consuming and labor-intensive. To address this, [PERSON] et al. [24] leveraged transfer learning and pseudo-labels, achieving high classification accuracy with minimal labeled samples. [PERSON] et al. [25] used distance transformation and auxiliary tasks to enhance the precision of surface defect detection, while [PERSON] et al. [26] applied a linear discriminant analysis to improve incremental learning. [PERSON] et al. [27] combined transfer learning with coordinate attention to address dataset insufficiencies, while [PERSON] et al. [28] utilized domain expert knowledge for high-fidelity image generation, improving multi-class task optimization. [PERSON] et al. [29] introduced a method that used shared weights for defect and non-defect image correlation, further improving model localization. Several studies have focused on defect detail extraction to detect subtle imperfections. [PERSON] et al. [30] developed a bio-inspired attention network based on retinal characteristics to enhance defect feature representation, while [PERSON] et al. [31] used dual-attention transformers to capture local defect details. [PERSON] et al. [32] proposed a linear transformation-based data augmentation method to emphasize defects, and [PERSON] et al. [33] utilized attention mechanisms for enhanced defect feature amplification. [PERSON] et al. [34] introduced a region-of-interest attention network for precise defect localization and differentiation. Despite their effectiveness in detecting common defects, these methods often fail to quantify defect severity, complicating quality assessment and hindering improvements in quality control.