# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 4 Results
### Ablation Studies
To verify the necessity of the pruning strategy, this study conducted ablation experiments by excluding the pruning operation to explore the relationship between segmentation performance and computational efficiency. The experimental results are presented in Table 5.  
Experimental results indicate that after incorporating the Carafe up-sampling, the model's accuracy significantly improved due to the optimization of the decoder, but the inference speed decreased substantially. After adding skip connections, the model's accuracy slightly declined, primarily because the skip connections introduced redundant information into the decoder, which negatively affected the segmentation performance. With the introduction of the HWD module and dynamic combined loss function, the model's accuracy continued to improve overall, and the final segmentation performance reached an MIoU of 80.15% and an F1-score of 77.64%. However, compared to the original model, the inference speed was significantly reduced to one-third of its original value, and the segmentation accuracy remained lower than the proposed improved model. The results of two ablation experiments demonstrate that pruning plays a beneficial role in crack segmentation tasks, helping to achieve a balance between model accuracy and inference speed. To enhance the interpretability of the experiments, Grad-CAM technology [39] was employed to visualize the original model and the proposed model. Grad-CAM generates a heatmap that reflects the regions of interest in the model by combining the feature maps from the last convolutional layer with the gradient information of the class prediction. This approach facilitates an intuitive understanding of the model's decision-making process. In the heatmap, warm colors indicate regions with high attention, while cool colors indicate regions with low attention. The visualization results are shown in Figure 8.  
The heatmap reveals that the original model exhibits low attention to the thinnest parts of fine cracks during recognition, a limitation that could result in fragmented crack detection outcomes. In contrast, the proposed model demonstrates superior performance in this regard, significantly improving the recognition of fine cracks. Moreover, the original model shows noticeably lower attention to crack edges compared to the proposed model. The latter effectively captures spatial details with greater precision, markedly enhancing attention to crack edges and enabling more accurate region segmentation. \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 \\ +Carafe Up-sampling & 79.57 & 76.97 & 97.50 & 4.34 & 5.99 & 38.16 \\ +Skip Connection & 79.45 & 76.88 & 97.50 & 4.34 & 6.02 & 38.01 \\ +HWD & 79.52 & 77.18 & 97.63 & 6.47 & 7.45 & 33.26 \\ +Dynamic Combined Loss Function & 80.32 & 78.01 & 97.55 & 6.47 & 7.45 & 33.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation Experiment Results After Excluding Pruning.