## 4 Experiments
### Datasets and Evaluation Metrics  
Before the work presented in [25], most models for pavement distress classification were evaluated using proprietary datasets. However, due to the limited size and inconsistent quality of such private datasets, we utilized the publicly available CQU-BPDD dataset. The dataset comprises 60,056 asphalt pavement images collected by professional inspection vehicles across multiple locations in South China at various times. It classifies pavement conditions into eight categories: transverse cracks, large cracks, crocodile cracks, crack filling, longitudinal cracks, wrinkles, repairs, and normal. Representative samples from the CQU-BPDD dataset are presented in Figure 4. These categories cover common types of pavement damage and include characteristics such as relatively small proportions of distressed areas, some of which may be in low-light conditions and difficult to identify. The CQU-BPDD dataset was initially divided into a training set and a test set. The training set includes 10,137 images, comprising 5137 abnormal pavement images (encompassing all damage categories) and 5000 normal images. The test set contains 49,919 images, with 11,589 labeled as abnormal and 38,330 as normal.  
In addition to employing the CQU-BPDD dataset as the primary experimental dataset, to further evaluate the generalizability and robustness of the proposed FFEAR model, the well-established Crack500 pavement crack dataset was included in the ablation study. As a benchmark dataset in pavement damage detection, Crack500 comprises 3363 diverse crack images. It is divided into training and testing subsets at a 7:3 ratio, with 2354 images used for training and 1005 reserved for evaluation. This division ensures a balanced approach to assessing model robustness across varying image samples.  
For comparative analysis with other pavement damage classification models, the CQU-BPMDD dataset was additionally employed. This dataset comprises 9851 disease images and 29,143 normal images, covering categories such as longitudinal cracks, transverse cracks, repairs, looseness, potholes, massive cracks, and wave crowding. Unlike CQU-BPDD, all images in CQU-BPMDD were captured with exposure compensation, and most depict small-scale cracks, making them more representative of real-world scenarios and posing greater challenges for detection. Representative samples from the CQU-BPMDD dataset are presented in Figure 5.  
The design of the fundamental parameters for all experiments is shown in Table 1. Accuracy, F1 score, and precision, commonly used metrics in image classification tasks, were employed as evaluation indicators.  
Accuracy represents the proportion of correctly classified samples out of the total number of samples, which is a comprehensive indicator to measure the accuracy of the model on all categories. It is described by Equation (8). Precision represents the proportion of samples truly belonging to a certain class among all samples predicted by the model to be of that class. Precision focuses on the correctness of the prediction results and how many of the predicted positive samples are truly positive, and it is described by Equation (9). F1 score integrates precision and recall and measures the model's performance through their  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Learning rate & 0.0001 \\ Weight decay & 0.0005 \\ Batch size & 32 \\ Epoch & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameter settings.  
Figure 4: Eight sample images from CQU-BPMD dataset.  
Figure 5: Four sample images from CQU-BPMDD dataset.  
harmonic mean, as shown in Equation (10).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[F1=2\times\frac{\frac{TP}{TP+FP}\times\frac{TP}{TP+FN}}{\frac{TP}{TP+FP}+ \frac{TP}{TP+FN}} \tag{10}\]