## 4 Experiments
### Experimental Analysis  
A summary of comparative experiments on the CQU-BPDD dataset is provided in Table 2. The models AlexNet [35], VGG16 [36], MobileNetV2 [37], MobileNetV3 [38], FFVT [39], TransFG [40], Vision Mamba [41], MambaOut [42], Vision Transformer, and Swin Transformer were comprehensively compared. The experimental results indicate that, compared to Swin-T-S (baseline), FFEAR achieves a 2.07% improvement in accuracy and a 0.97% increase in F1 score, with similar precision. To demonstrate the superior performance of FFEAR in pavement damage classification, we selected professional pavement breakage classification models of PiCT [25], DMTC [43], and DDACDN [44] to compare with FFEAR. Overall, FFEAR demonstrates strong performance in the road damage detection and classification task.  
As shown clearly in Table 2, FFEAR performs better than other classical classification models on the main evaluation indicators. Due to the special characteristics of road surface damage images, there are some drawbacks in directly applying the classical classification model to this task, so the performance is not as good as that of FFEAR. Firstly, we introduce FFE to solve the difficulties of the low damage ratio and high similarity of damage types in road surface damage images and use depthwise separable convolution for secondary feature extraction to enhance the feature expression ability. Secondly, MHAR is introduced to address issues such as uneven lighting and background noise interference. The features extracted twice by the fine feature extraction module are re-embedded using multi-head attention, which not only strengthens the representation of limited features but also mitigates the challenges posed by background noise. These improvements make FFEAR achieve better performance in road surface damage classification tasks. FFEAR exhibits a 2.07%  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **F1 Score** \\ \hline MobileNetV3 & 82.31\% & 79.91\% & 79.90\% \\ MobileNetV2 & 83.40\% & 79.55\% & 79.71\% \\ AlexNet & 73.43\% & 75.16\% & 73.48\% \\ VGG16 & 77.23\% & 84.63\% & 79.45\% \\ Swin-T-S & 87.43\% & **88.65**\% & 87.50\% \\ Swin-T-B & 87.88\% & 88.34\% & 87.85\% \\ ViT-B-16 & 86.08\% & 85.96\% & 85.76\% \\ ViT-B-32 & 84.39\% & 82.37\% & 82.99\% \\ Vision Mamba & 87.12\% & 88.21\% & 87.33\% \\ MambaOut & 87.37\% & 88.61\% & 87.63\% \\ FFVT & 81.98\% & 81.85\% & 81.26\% \\ TransFG & 80.88\% & 80.87\% & 80.22\% \\ DMTC & 84.32\% & 83.92\% & 82.68\% \\ DDACDN & 83.62\% & 83.73\% & 83.57\% \\ PicT & 88.58\% & 88.14\% & 87.85\% \\
**FFEAR** & **89.24**\% & 88.36\% & **88.35**\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experimental data on CQU-BPDD dataset.  
improvement in accuracy and a 0.97% increase in F1 score, with similar precision to Swin-T. Compared with the ViT, FFEAR achieves a 3.67% improvement in accuracy, a 3.02% increase in F1 score, and a 2.8% increase in precision.  
To further highlight the strong performance of FFEAR in pavement damage classification, comparisons were conducted with several specialized pavement damage classification models. The experimental results, presented in Figure 6, demonstrate that FFEAR outperforms comparable models across multiple evaluation metrics. By incorporating FFE and MHAR, FFEAR effectively leverages limited features to achieve superior classification accuracy.  
In order to verify the robustness and generalization ability of FFEAR, we conducted generalization experiments with other pavement damage classification models on the CQU-BPMDD dataset. The experimental results are shown in Table 3. Compared with the baseline on CQU-BPMDD dataset, FFEAR achieved a 0.92% improvement in accuracy, a 1.93% improvement in precision, and a 2.4% improvement in F1 score. These results also prove that FFEAR is superior to other similar models.  
Figure 6: Comparison of FFEAR and pavement damage classification models on CQU-BPMDD dataset.