## 2 Methodology
### Foreground Focus Module  
The foreground focus module highlights regions in the image that are likely to contain pavement distress. It directs the model's attention to areas with higher probabilities of distress while ignoring background regions. The background elements of the collected road images typically exhibit significant variation. These differences can often confuse the model's understanding of pavement distress, affecting the transfer performance of the pavement distress detection model in the target domain. Additionally, the location distribution of pavement distress on images in the target domain may also differ from that in the source domain. This can lead to the pavement distress detection model trained in the source domain ignoring some pavement distress in the target domain as invalid information, because the model has never encountered pavement distress appearing in certain locations in the source domain. Therefore, when applying a well-trained model from the source domain to the target domain, it is necessary to adjust the model's attention to focus on regions where pavement distress is more likely to occur. Figure 5: The illustration of the viewpoint transformation module. The attention mechanism is a deep learning module that mimics how humans process external information. It allows the model to focus on prominent features in input images for key calculations and predictions, enabling the computer to focus on the characteristics of the key regions quickly and efficiently. The attention mechanism can be abstracted into the following expression:  
\[Attention=f\left(g(x),x\right) \tag{3}\]  
where \(x\) represents the input features; \(g\) represents the processing of input features to generate attention; \(f\) represents the processing of input features combined with attention. This attention mechanism can be seen as a dynamic weight adjustment process based on input image features. [PERSON] et al. [28] first introduced the attention mechanism into computer vision and proposed a novel non-local network, greatly improving the accuracy of object detection. Furthermore, [PERSON] et al. [29] introduced the transformer architecture, which achieved tremendous success in natural language processing, into computer vision. Leveraging the powerful self-attention capability of the transformer, they developed the Vision Transformer model, which possesses enhanced image recognition capabilities. Cracks in pavement often appear as elongated shapes, with their effective features concentrated along the crack lines, especially in alligator cracks. Similarly, other distress types like potholes are typically smaller, with their features concentrated locally. Therefore, to further enhance the cross-scene transfer ability of pavement distress recognition models, focusing the model's attention on prominently featured pavement distress, introducing an attention mechanism is an important step. To achieve this, this study employs the domain prior knowledge generator to extract attention points from the current images in the target domain. These attention points represent areas identified by the domain prior knowledge generator as requiring significant focus, which may indicate the presence of pavement distress. Subsequently, considering the randomness inherent in the domain prior knowledge generator, not all attention points are useful. Therefore, k-means clustering is utilized to cluster these attention points, yielding several cluster centers that serve as highly reliable attention points for pavement distress identification. Finally, the average distance from all pixels in the image to these cluster centers is calculated, which is used as the attention weight for the image. This weight is then multiplied by the original image to obtain the foreground-focused target domain image, as illustrated in Figure 6. The pseudocode of the foreground focus module is presented as Algorithm 3. ```
Input: The Attention Points \(P\), the Input Image \(X\). Output: The Foreground Focus Image \(X^{\prime}\).