# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
## 5 Results and Discussion  
### Influence of Semantic Labels and Transfer Learning on Model Performance  
This section investigates the impact of semantic labeling granularity and transfer learning strategies on the performance of segmentation models. We evaluated four mainstream architectures--U-Net, DeepLabv3+, HRNet, and the proposed ETAFHrNet--across datasets annotated using both two-class and three-class schemes (see Table 2). The two-class scheme included only background and crack categories, while the three-class variant further distinguished between linear cracks and alligator (reticular) cracks. The findings indicate that models trained using the two-class scheme consistently surpass those trained with the three-class approach. For example, U-Net exhibited improvements of around 3.2% in mIoU and 4.13% in F1-score when utilizing the two-class dataset. DeepLabv3+ displayed greater robustness to label granularity, with metric fluctuations remaining within a 3% margin. Notably, ETAFHrNet achieved the best results under the two-class setting, reaching an mIoU of 74.41% and an F1-score of 83.11%. This superior performance can be attributed to ETAFHrNet's architectural design, which emphasizes long-range dependency modeling and directional feature enhancement. These mechanisms are especially effective when the task is simplified to binary segmentation, allowing the model to focus entirely on the structural continuity and contextual consistency of cracks without being distracted by inter-class ambiguity. In contrast, the three-class setting introduces greater intra-class variability and semantic overlap, which can interfere with feature representation and degrade performance. These findings suggest that the simplified two-class labeling strategy is more appropriate for practical crack detection tasks. Finer-grained class distinctions often introduce class imbalance and inter-class confusion [57, 58], while offering limited added value in most real-world engineering applications. We further evaluated the effect of transfer learning, specifically pre-training on a large-scale dataset followed by fine-tuning on a smaller, task-specific dataset, to assess its influence on model performance (see Table 3). The results demonstrate that this strategy leads to consistent and significant improvements across all evaluated models. Notably, ETAFHrNet achieved an 8.09% increase in mean Intersection over Union (mIoU), reaching a peak value of 74.4%, thereby outperforming all other models by a substantial margin. U-Net and SegFormer also benefited from transfer learning, with respective gains of 3.73%and 5.63% in mIoU, further validating the effectiveness of this approach [59]. It is worth noting that Transformer-based architectures, particularly CNN-Transformer hybrid models, typically lack inherent spatial inductive biases and often require large-scale training data to achieve optimal performance. Consequently, pre-training plays a critical role in enabling these models to generalize effectively, especially when applied to smaller, domain-specific datasets, such as those used for pavement crack segmentation. Based on the experimental findings, it is evident that both simplified semantic labeling and transfer learning substantially enhance segmentation performance, with ETAFhr-Net consistently demonstrating the strongest results across evaluation metrics. Notably, the transfer learning setup involved pre-training on a composite dataset that included publicly available sources (e.g., CRACK500, GAPs384), before fine-tuning on QD-Crack. This configuration simulates cross-domain adaptation and indirectly reflects the model's ability to generalize beyond a localized dataset. To further investigate the critical factors influencing feature extraction and multi-scale fusion, and to conduct in-depth comparisons with alternative network architectures, we adopt the two-class labeling scheme and apply transfer learning as the default training strategy in all subsequent ablation and comparative experiments.