# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 4 Results
### Ablation Studies  
To evaluate the impact of various improvements on model performance, this paper uses the FCN8s model with EfficientNet-B0 as the backbone and progressively introduces enhancements, including pruning, the Carefe up-sampling module, additional skip connections, the HWD module, and a dynamic combined loss function. Furthermore, the Carefe up-sampling, skip connections, the HWD module, and dynamic combined loss function are added individually to the pruned model to better assess the contribution of each improvement to the model's final performance. Ablation experiments are conducted on the Crack500 dataset to verify the effectiveness of these improvements, with the results presented in Table 4.  
After the pruning operation, the segmentation performance of the model declined significantly. To address this issue, the Carefe up-sampling module, skip connections, the HWD module, and dynamic combined loss function were individually incorporated into the pruned model. Each improvement contributed to enhancing the segmentation accuracy to varying extents. Furthermore, by progressively integrating all proposed enhancements, the segmentation performance achieved consistent improvements, fully validating the effectiveness of the proposed method. Ultimately, the model achieved an MIoU of 80.37% and an F1-score of 78.22%, which represent increases of 3.08% and 5.62%, respectively, compared to the original model, while significantly reducing the parameter count and computational cost. Although the inference speed decreased, the model still satisfies the requirements for real-time road crack segmentation. The reduction in frame rate can be partly attributed to GPUs being more optimized for standard convolutions and other conventional operations, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **MIoU/\%** & **F1-Score/\%** & **Accuracy/\%** & **Params/M** & **Flops/G** & **FPS** \\ \hline Baseline & 77.29 & 72.60 & 97.28 & 3.59 & 2.14 & 107.35 \\ +Pruning & 71.88 & 65.19 & 96.49 & 0.13 & 0.35 & 151.54 \\ \hline Only + Cerate Up-sampling & 78.66 & 75.32 & 97.36 & 0.18 & 0.81 & 81.19 \\ Only + Skip Connection & 74.69 & 69.74 & 96.74 & 0.13 & 0.36 & 147.40 \\ Only + HWD & 76.67 & 72.70 & 97.09 & 0.19 & 0.48 & 121.84 \\ Only + Dynamic Combined Loss Function & 75.56 & 71.63 & 96.55 & 0.19 & 0.48 & 121.84 \\ \hline +Carafe Up-sampling & 78.66 & 75.32 & 97.36 & 0.18 & 0.81 & 81.19 \\ +Skip Connection & 79.14 & 76.01 & 97.42 & 0.18 & 0.82 & 80.77 \\ +HWD & 79.34 & 76.45 & 97.50 & 0.23 & 0.95 & 73.49 \\ +Dynamic Loss Function & 80.37 & 78.22 & 97.59 & 0.23 & 0.95 & 73.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment Results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loss** & **Weighting Strategy** & **MIoU/\%** & **F1-Score/\%** & **Accuracy/\%** \\ \hline \(CL\) & Fixed Weight & 79.34 & 76.45 & 97.50 \\ \(DL\) & Fixed Weight & 79.14 & 76.03 & 97.51 \\ \(FL\) & Fixed Weight & 73.62 & 68.49 & 95.63 \\ \(CL+DL\) & Fixed Weight & 79.91 & 77.70 & 97.48 \\ \(CL+DL+FL\) & Fixed Weight & 79.80 & 77.37 & 97.45 \\ \(CL+DL+FL\) & Dynamic Weight, \(\beta\in[0.0,1.0]\) & 79.97 & 77.86 & 97.35 \\ \(CL+DL+FL\) & Dynamic Weight, \(\beta\in[0.4,0.6]\) & 79.90 & 77.62 & 97.42 \\ \(CL+DL+FL\) & Dynamic Weight, \(\beta\in[0.3,0.7]\) & 80.07 & 78.00 & 97.43 \\ \(CL+DL+FL\) & Dynamic Weight, \(\beta\in[0.1,0.9]\) & 80.10 & 77.78 & 97.50 \\ \(CL+DL+FL\) & Dynamic Weight, \(\beta\in[0.2,0.8]\) & 80.37 & 78.22 & 97.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Loss Function Comparison Experiments. timization support for complex modules like HWD and Carafe up-sampling. Future improvements using tools such as TensorRT and ONNX could further enhance the inference speed. Previous studies [22; 27] have indicated that networks in crack segmentation tasks are prone to parameter redundancy, which can negatively impact accuracy.