# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
## 5 Results and Discussion
### Ablation Experiment  
To verify the synergistic contribution of the proposed CSHAM and EHAT modules to pavement crack segmentation performance, we conducted a series of systematic ablation experiments on a benchmark crack detection dataset. By progressively removing or \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Classes** & **mIoU (\%)** & **mRecall (\%)** & **mPrecision (\%)** & **F1-Score (\%)** \\ \hline \multirow{2}{*}{U-Net} & two & 62.85 & 70.76 & 71.23 & 71.90 \\  & three & 59.62 & 67.48 & 68.07 & 67.77 \\ \hline \multirow{2}{*}{DeepLabv3\_Plus} & two & 65.72 & 73.25 & 73.86 & 73.92 \\  & three & 62.47 & 70.09 & 70.54 & 70.31 \\ \hline \multirow{2}{*}{HRNet} & two & 63.49 & 72.98 & 70.34 & 71.58 \\  & three & 60.15 & 69.11 & 67.32 & 68.20 \\ \hline \multirow{2}{*}{ETAFHNet} & two & 74.41 & 83.84 & 84.51 & 83.11 \\  & three & 71.08 & 79.21 & 76.64 & 79.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of semantic segmentation model performance across multiple datasets. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Transfer Learning** & **mIoU (\%)** & **mRecall (\%)** & **mPrecision (\%)** & **F1-Score (\%)** \\ \hline \multirow{2}{*}{U-Net} & No & 59.12 & 66.32 & 67.28 & 67.45 \\  & Yes & 62.85 & 70.76 & 71.23 & 71.90 \\ \hline \multirow{2}{*}{DeepLabv3\_Plus} & No & 61.47 & 69.15 & 69.87 & 69.90 \\  & Yes & 65.72 & 73.25 & 73.86 & 73.92 \\ \hline \multirow{2}{*}{HRNet} & No & 60.08 & 68.44 & 68.93 & 68.71 \\  & Yes & 63.49 & 72.98 & 70.34 & 71.58 \\ \hline \multirow{2}{*}{SegFormer} & No & 63.58 & 71.72 & 72.13 & 72.25 \\  & Yes & 69.21 & 76.52 & 76.88 & 76.73 \\ \hline \multirow{2}{*}{ETAFHrNet} & No & 66.32 & 74.65 & 75.15 & 75.38 \\  & Yes & 74.41 & 83.84 & 84.51 & 83.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of transfer learning experiments. replacing key architectural components, we quantitatively assessed the impact of each module on both segmentation accuracy and inference efficiency, measured in Frames Per Second (FPS). As shown in Table 4 and Figure 9, the baseline model--comprising solely the original HRNet without integration of the CSHAM or EHAT modules--achieves an mIoU of 63.49%, with corresponding mPrecision and mRecall scores of 72.98% and 70.34%, respectively. The F1-score falls to 71.58%, and the inference speed is recorded at 14.51 FPS. These results indicate that conventional multi-scale fusion mechanisms, as employed in HRNet, are inadequate for capturing the elongated, fine-grained, and morphologically diverse structures characteristic of pavement cracks. Moreover, the visual outputs shown in Figure 10 reveal pronounced discontinuities and susceptibility to background noise in the baseline predictions, resulting in coarse segmentation contours and inconsistent structural delineation. These findings further underscore the importance of enhancing both feature fusion and directional awareness for high-precision crack segmentation. From a resource perspective, the baseline HRNet contains 45.0 M parameters. Adding EHAT or CSHAM alone keeps the footprint below 48.5 M while lifting mIoU by at least 7 percentage points and increasing throughput by 60%. Activating both modules brings the total to only 50.6 M parameters (+12%) yet almost doubles FPS (14.51 to 28.56) and raises mIoU by 10.9 percentage points, delivering the best accuracy-efficiency balance. When the EHAT module is introduced independently, the model achieves an mIoU of 70.18%, with mPrecision and mRecall reaching 81.08% and 77.05%, respectively. The F1-score rises to 78.93%, and the inference speed improves to 22.83 FPS. As shown in Figure 10, the inclusion of axial positional encoding and local window attention enhances the model's ability to capture directionally oriented crack features, resulting in more continuous and clearly delineated crack contours.