# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
## 4 Experimental Details
### Training Parameters and Methods  
To ensure the accuracy and reproducibility of the experiments, the detailed configuration of the experimental environment is summarized in Table 1.  
During the experimental procedure, the dataset was divided into a training set and a test set at a fixed ratio of 8:2. Model parameters were iteratively optimized using the training set, whereas the test set was reserved for assessing generalization capability. To efficiently manage GPU memory limitations, the batch size was configured to 8, enabling effective utilization of the available computational resources. Based on prior experimental experience, the number of training epochs was uniformly set to 120, as the loss function consistently converged near this point across multiple configurations. The convergence behavior is visualized in Figure 8, which illustrates the decline and stabilization of the loss function across epochs. The model achieves a stable convergence state by approximately the 120 th epoch, validating the effectiveness of the selected training schedule.  
In image segmentation tasks, accurate delineation of object contours often relies on spatially consistent feature distributions. Building upon this observation, we incorporated a transfer learning strategy to enhance model learning efficiency. The core principle of transfer learning lies in reusing knowledge--specifically, pre-trained model weights--from one task to accelerate learning in a related but distinct target task, much like how humans transfer prior experience to new problems. In this work, the model parameters were initially  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Configuration Items** & **Configuration** \\ \hline Operating System & Windows 11 \\ Deep Learning Framework & PyTorch 1.10.0 \\ Processor & Intel Core i7-12700k \\ RAM & 32 GB \\ GPU & NVIDIA GeForce RTX 3070 Ti (8 GB) \\ GPU Memory & 8 GB \\ CUDA Version & 11.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model training configuration.  
Figure 8: The trend of loss function with the number of training rounds.  
pre-trained on a large-scale crack detection dataset, and subsequently fine-tuned on the target dataset to adapt to the specific task requirements.  
The SDNET2018 dataset [52] was selected as the source for pre-training. Comprising over 56,000 annotated concrete crack images, SDNET2018 is widely recognized in the field for its utility in training, validation, and benchmarking of crack detection algorithms. Experimental results demonstrate that this transfer learning approach [53] not only accelerates convergence but also yields significant improvements in segmentation accuracy. Owing to the visual feature similarities shared across diverse real-world objects, this strategy closely aligns with human perceptual learning processes.  
To fully exploit the benefits of pre-trained knowledge, we adopted a freeze-thaw training strategy [54], as opposed to random weight initialization. Given that the network backbone is responsible for extracting generalizable low-level features, its parameters were initially frozen, while the remaining layers were fine-tuned on the target data. During the mid-to-late training phases, the backbone was gradually unfrozen to allow full network optimization and better task adaptation.  
The initial learning rate was set to 0.0001 and dynamically adjusted using a cosine annealing schedule [55] to improve convergence stability and efficiency. To further stabilize training, we set the momentum parameter to 0.975 and employed the Adam optimizer [56], which adaptively adjusts learning rates by incorporating both first- and second-order moment estimates of the gradients.