# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
## 5 Results and Discussion
### Comparison with Existing Advanced Methods
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **mIoU (\%)** & **mPrecision (\%)** & **mRecall (\%)** & **F1-Score (\%)** & **FPS** & **Params (M)** \\ \hline U-Net & 62.85 & 70.76 & 71.23 & 71.90 & 15.37 & 31.0 \\ HRNet & 63.49 & 72.98 & 70.34 & 71.58 & 14.51 & 45.0 \\ PSPNet & 64.32 & 71.85 & 72.64 & 72.24 & 17.22 & 42.6 \\ DeepLabv3+ & 65.72 & 73.25 & 73.86 & 73.92 & 19.84 & 42.0 \\ SegFormer & 69.21 & 76.52 & 76.88 & 76.73 & 21.34 & 64.0 \\
**ETAFhrNet** & **74.41** & **83.84** & **84.51** & **83.11** & **28.56** & 50.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of various semantic segmentation models on the QD-Crack dataset. (Bold text highlights better model parameters). faint, low-contrast cracks through adaptive multi-scale fusion, effectively mitigating the information loss seen in DeepLabv3+, which relies on a single-path fusion mechanism. In more visually complex backgrounds, such as those in groups 2 and 3, the integration of EHAT and CSHAM enhances both crack-to-background contrast and edge localization precision. By comparison, U-Net frequently exhibits crack discontinuities, attributed to its limited receptive field, while PSPNet often generates over-smoothed or mis-clustered predictions due to its coarse context modeling during feature fusion. These qualitative results further reinforce the quantitative superiority of ETAFhrNet in accurately segmenting diverse and challenging crack patterns. Nevertheless, Figure 13 also reveals that ETAFhrNet is not flawless. (1) Sample a: the predicted transverse (horizontal) crack appears noticeably blurred compared with the sharper boundary produced by DeepLabv3+, indicating a tendency towards over-smoothing along horizontal orientations. (2) Sample c: the forked crack at the bottom is segmented with exaggerated width, resulting in an over-emphasized branch. These failure cases highlight the remaining optimization space for edge-preservation and scale-aware refinement. Although the predicted segmentation maps from different models may appear visually similar in some cases, high-precision crack pattern identification plays a critical role in pavement management. Distinguishing between transverse and alligator cracks, for instance, informs whether surface sealing or full-depth patching is required. Precise segmentation also improves damage quantification, enabling more accurate cost estimation, lifecycle prediction, and prioritization of maintenance resources. In summary, ETAFhrNet, empowered by its innovative hybrid attention mechanisms and adaptive multi-scale fusion strategy, delivers substantial improvements in segmentation accuracy, robustness, and computational efficiency for pavement crack detection. The model exhibits clear advantages in crack-structure preservation, background-noise suppression, and fine-detail restoration, underscoring its strong potential for deployment in real-world road-inspection and maintenance scenarios. Figure 13: Segmentation outcomes comparison among different models. (**a–d**) Randomly sampled images from the dataset. The red boxes indicate regions where differences occur.