# Transfer Learning Model for Crack Detection in Side SlopesBased on Crack-Net
## 4 Experiment
### Comparative Analysis  
The effectiveness of the proposed improvement to the model was determined through a comparative experimental analysis of seven distinct models. The models selected for this study included U-Net [35], SegNet [36], DeepLab [37], DeepLabV3 HfSegNet-B16 [38], CrackNex [39], and CrackFormer [40]. The evaluation was conducted on a custom-built slope crack dataset. The experimental results demonstrate that the Crack-Net model proposed in this paper exhibits superior comprehensive performance in multi-indicator evaluation, as evidenced by its highest AP value in Table 2 compared to baseline models such as U-Net and DeepLabV3. The AP value of the CrackFormer model is 5.2% higher than that of the proposed model, which highlights the effectiveness of the frequency-domain nonlinear mapping module in separating background noise from crack features. In addition, the deep feature fusion module improves subpixel boundary localization through cross-layer feature alignment, which significantly increases model accuracy while maintaining a high recall rate. As well as AP, the proposed model achieves the highest F1 score (0.833) and mIoU (0.865), demonstrating superior performance in both segmentation accuracy and region overlap. Furthermore, the proposed model contains only 22.3 million parameters, which is fewer than most comparison models. Moreover, the findings from the transfer learning experiment (Table 3) demonstrate that, upon pre-training, the AP value exhibited an enhancement from 92.1% to 93.7%; regarding convergence speed, the transfer learning group reached convergence in 73 epochs, whereas the non-transfer learning group required 126 epochs. This represents a 42.1% improvement in convergence efficiency, and the training time was reduced from 8.7 h to 4.2 h. These results indicate that pre-trained weights provide a more stable optimization path. Overall, these findings demonstrate that the transfer learning strategy improves training efficiency and model stability and confirm the practical value of the proposed method for slope crack detection with limited sample sizes. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **P** & **R** & **AP** & **ODS** & **OIS** & **F1** & **mIoU** & **Params (M)** \\ \hline U-Net & 0.681 & 0.716 & 0.762 & 0.665 & 0.684 & 0.698 & 0.761 & 31.0 \\ SegNet & 0.674 & 0.806 & 0.771 & 0.689 & 0.704 & 0.734 & 0.758 & 29.5 \\ DeepLab & 0.694 & 0.802 & 0.793 & 0.697 & 0.714 & 0.744 & 0.772 & 26.6 \\ DeepLabV3 & 0.712 & 0.810 & 0.827 & 0.715 & 0.735 & 0.758 & 0.785 & 11.38 \\ HfSegNet-B16 & 0.748 & 0.813 & 0.844 & 0.734 & 0.757 & 0.779 & 0.784 & 9.51 \\ CrackNex & 0.776 & 0.808 & 0.853 & 0.757 & 0.789 & 0.792 & 0.801 & 28.3 \\ CrackFormer & 0.780 & 0.818 & 0.869 & 0.764 & 0.797 & 0.799 & 0.843 & 25.4 \\ Ours & 0.823 & 0.844 & 0.921 & 0.817 & 0.841 & 0.833 & 0.865 & 22.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Contract test. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Experimental Group** & **P** & **R** & **AP** & **ODS** & **OIS** & **Convergence Epochs** & **Training Time (h)** \\ \hline No transfer learning & 0.823 & 0.844 & 0.921 & 0.817 & 0.841 & 126 & 8.7 \\ With transfer learning & 0.854 & 0.847 & 0.937 & 0.821 & 0.845 & 73 & 4.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the transfer learning experiment. As demonstrated in Figure 8, a comparative analysis was conducted on the mask results of the proposed model and other state-of-the-art methods, namely U-Net, SegNet, DeepLabV3, CrackNex, and CrackFormer, on a custom dataset comprising cracks on slopes. The results reveal that U-Net exhibits suboptimal performance in detecting cracks, with incomplete detection outcomes when compared to other models. The detection model proposed in this paper exhibits enhanced performance compared to other models, yet there remains scope for refinement to approach the precision of the original mask image.