# Transfer Learning Model for Crack Detection in Side SlopesBased on Crack-Net
## 5 Conclusions  
This paper presents a Crack-Net model based on a transfer learning framework to address challenges in slope crack detection. Pre-training on a large crack dataset and subsequently transferring the model to a smaller, target-specific dataset significantly enhances its generalizability. This improvement is particularly evident when only limited samples are available. The model incorporates a novel frequency-domain nonlinear mapping module  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **FDNM** & **DFFM** & **P** & **R** & **AP** & **ODS** & **OIS** & **FPS** \\ \hline Original & \(\times\) & \(\times\) & 0.741 & 0.819 & 0.851 & 0.731 & 0.762 & 22.9 \\ Only FDNM & ✓ & \(\times\) & 0.789 & 0.835 & 0.884 & 0.779 & 0.811 & 25.5 \\ Only DFFM & \(\times\) & ✓ & 0.816 & 0.821 & 0.892 & 0.774 & 0.815 & 26.7 \\ Full model & ✓ & ✓ & 0.834 & 0.846 & 0.912 & 0.808 & 0.830 & 27.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment.  
Figure 12: Distribution analysis of performance metrics across experimental settings.  
to address the challenges posed by the irregular and sporadic nature of cracks. This module enhances the expressiveness and diversity of crack features through frequency-domain feature enhancement, enabling the model to detect and recognize even minimally sized cracks. In addition, a novel deep feature fusion module is proposed to improve feature interconnectivity and integration between the encoder and decoder. This module further enhances crack feature representation, suppresses irrelevant features, and increases the model's robustness.  
The Crack-Net model performs optimally on slope surfaces that are relatively clean, with moderate-to-steep gradients, adequate natural lighting, and minimal interference from vegetation or shadows. Under these conditions, crack features are more prominent and can be effectively extracted. Conversely, detection accuracy may decline in environments with dense vegetation, significant shadowing, low-light conditions, or adverse weather such as heavy rain or fog, as these factors can obscure or distort crack features. Future research will focus on further optimizing the Crack-Net architecture and exploring advanced feature extraction and integration techniques. Addressing these challenges and further enhancing the model's robustness and adaptability to complex real-world environments will be a key direction for future research. These improvements aim to further enhance the model's performance in slope crack detection. Additionally, the model will be rigorously tested in diverse, practical application scenarios. Crack-Net is expected to provide reliable and intelligent technical support for slope safety monitoring, thereby contributing to reductions in geological disasters such as landslides and enhancing the protection of lives and property.  
Conceptualization, Y.Z.; methodology, N.L. and Y.Z.; investigation, N.L. and Y.Z.; resources, Q.Z. and S.Z.; writing--original draft preparation, N.L.; writing--review and editing, N.L., Y.Z., Q.Z. and S.Z.; visualization, Y.Z.; supervision, Q.Z. and S.Z.; project administration, N.L. All authors reviewed the results and approved the final version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 62002285) and the Young Innovation Team of Shaanxi Universities.  
Not applicable.  
The public CrackVision12k dataset can be downloaded from [[https://www.kaggle.com/datasets/vangiap/crackvision12k/data](https://www.kaggle.com/datasets/vangiap/crackvision12k/data)]([https://www.kaggle.com/datasets/vangiap/crackvision12k/data](https://www.kaggle.com/datasets/vangiap/crackvision12k/data)) (accessed on 17 December 2024). As the customized dataset used in this study is not easily accessible and is subject to third-party, commercial, or privacy restrictions, interested researchers may contact the corresponding author via email to discuss data access.  
The authors declare no conflicts of interest.