## 4 Experiments
### Ablation Analysis  
FFE and MHAR in the proposed FFEAR model are specifically designed to tackle the distinct challenges of pavement damage images, including low damage ratios, uneven illumination, similar crack patterns, and high visual similarity among samples. The corre  
Figure 7: Visual analysis of heat maps. The first line of pictures is the pavement damage image, where the damage is marked by a red box. The second line is a visual analytical heat map of Swin-T. The third line is a visual analytical heat map of FFEAR.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **F1 Score** \\ \hline Swin-T & 85.63\% & 84.72\% & 83.91\% \\ DMTC & 84.32\% & 83.92\% & 82.68\% \\ DDACDN & 81.62\% & 81.37\% & 80.93\% \\
**FFEAR** & **89.24**\% & 88.36\% & **88.35**\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of generalization experiments on CQU-BPMDD dateset.  
sponding ablation study results are provided in Table 4, with a histogram illustrating these results shown in Figure 8.  
The experimental results indicate that using MHAR in isolation yields limited improvement in evaluation metrics. This limitation is attributed to the module's focus on re-embedding rather than re-extracting fine features from the pavement damage images, relying primarily on original convolutional outputs. As a result, MHAR remains par  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Model** & **Accuracy** & **Precision** & **F1 Score** & **Param (M)** \\ \hline \multirow{4}{*}{CQU-BPDD} & Swin-T & 87.43\% & **88.65**\% & 87.50\% & 48 \\  & MHAR & 87.11\% & 85.51\% & 85.61\% & 58 \\  & FFE & 88.14\% & 88.30\% & 87.66\% & 48 \\  & **FFEAR** & **89.24**\% & 88.36\% & **88.35**\% & 58 \\ \hline \multirow{4}{*}{Crack500} & Swin-T & 76.82\% & 76.64\% & 76.37\% & 48 \\  & MHAR & 76.52\% & 76.77\% & 76.42\% & 58 \\ \cline{1-1}  & FFE & 77.24\% & 77.10\% & 76.50\% & 48 \\ \cline{1-1}  & **FFEAR** & **77.71**\% & **77.65**\% & **76.80**\% & 58 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experimental results based on CQU-BPDD dataset and Crack500 dataset.  
Figure 8: Histogram of ablation experiment; (**a**) is the results on the CQU-BPDD dataset, and (**b**) is the results on Crack500 dataset.  
tial, reducing the model's representational capacity and limiting its focus on intricate damage details.  
When employing only FFE, greater emphasis is placed on small-scale damage features, enhancing feature expression and maximizing their representation. The experimental results indicate that the proposed method achieves improvements across all evaluation metrics.  
When combining the fine feature extraction and multi-head self-attention re-embedding modules, the model shows an accuracy increase of 2.07% and an F1 score improvement of 0.97% in the experimental results, achieving superior performance compared to using either module individually.  
In terms of performance and efficiency comparison, the baseline Swin-T model has a parameter count of 48 M. After the MHAR module and the FFE module are incorporated separately, the parameter counts increase to 58 M and 48 M, respectively. When the two modules are integrated into the full FFEAR model, the total number of parameters reaches 58 M. Although the structural enhancements introduce a moderate increase in parameter count, the computational complexity remains at \(O(n^{2})\), without adding additional computational burden. This controlled increase in parameters leads to a consistent improvement in classification performance. The experimental results demonstrate that the FFEAR model outperforms the baseline in both accuracy and F1 score, confirming the effectiveness and practicality of the proposed design.