## 5 Conclusions  
In this paper, we establish a DCNN based on a semi-supervised learning method for anomaly crack detection to address the challenges posed by the difficulty of obtaining crack data for in-service infrastructure. Some examples are used to demonstrate the high efficiency and excellent performance of the trained model in addressing crack detection problems, including:  
Figure 16: Confusion matrix of pavement testing set images. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Sets** & **Backbone Network** & **AUC** & **Thermal Threshold** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{Wall} & VGG-16 & 0.9998 & 0.2293 & 98.86\% & 100.00\% & 98.12\% & 99.05\% \\ \cline{2-7}  & AlexNet & 0.9756 & 0.1568 & 95.65\% & 98.65\% & 96.32\% & 97.48\% \\ \cline{2-7}  & ResNet-18 & 0.9903 & 0.2365 & 97.86\% & 100.00\% & 97.04\% & 98.50\% \\ \cline{2-7}  & ResNet-34 & 0.9964 & 0.1985 & 98.37\% & 100.00\% & 97.86\% & 98.89\% \\ \hline \multirow{4}{*}{Bridge} & VGG-16 & 0.9701 & 0.0944 & 91.52\% & 96.03\% & 89.24\% & 92.48\% \\ \cline{2-7}  & AlexNet & 0.9056 & 0.0539 & 83.96\% & 91.26\% & 79.09\% & 84.75\% \\ \cline{2-7}  & ResNet-18 & 0.9568 & 0.1232 & 89.65\% & 87.25\% & 93.74\% & 90.36\% \\ \cline{2-7}  & ResNet-34 & 0.9365 & 0.0789 & 86.32\% & 93.72\% & 82.08\% & 87.51\% \\ \hline \multirow{4}{*}{Pavement} & VGG-16 & 0.9885 & 0.0985 & 96.11\% & 94.08\% & 93.71\% & 93.80\% \\ \cline{2-7}  & AlexNet & 0.9563 & 0.0626 & 89.36\% & 90.17\% & 85.34\% & 87.69\% \\ \cline{2-7}  & ResNet-18 & 0.9875 & 0.1259 & 94.53\% & 92.47\% & 90.53\% & 91.49\% \\ \cline{2-7}  & ResNet-34 & 0.9899 & 0.0768 & 95.69\% & 96.95\% & 87.59\% & 92.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Experimental results of different backbone networks in each dataset. 1. The DCNN is used to realize automatic crack detection, and the influence of subjective factors can be removed. 2. Only a small number of labeled datasets are required to train the semi-supervised model, and the computation time and manpower for labor-intensive labeling tasks are greatly reduced. 3. The model is tailor-made for crack detection in infrastructure and has good robustness for large changes in structural roughness, uneven illumination and crack differences. 4. The neural network can be visualized to describe its working principle, in terms of the heatmaps of different testing results. 5. The effectiveness of the DCNN based on a semi-supervised learning method for anomaly crack detection is validated by several experiments. The method can be used to address the issues of the lack of crack data and interfering with non-crack data effectively. It offers great potential for further applications in engineering practice. In the future, we will conduct a study on how to use the semi-supervised learning anomaly detection method based on a DCNN in more complex backgrounds and multi-defect classification detection. Image segmentation is the process of dividing an image into meaningful pixel-level regions. It is one of the important tasks for crack shape estimation. In future work, we will also explore image segmentation techniques; therefore, classification accuracy can be enhanced. X.G. : Conceptualization, Methodology Software, Data curation, Writing--original draft preparation, Visualization and Funding acquisition. C.H. : Writing--original draft preparation and Data curation. S.T. : Conceptualization, Methodology, Investigation, Visualization, Writing--original draft preparation and Writing--review and editing. G.C. : Writing--original draft preparation, Writing--review and editing and Project administration. All authors have read and agreed to the published version of the manuscript. This research was funded by [the National Natural Science Foundation of Chinal grant number [51808135] and [Youth Fund of Guangdong University of Technology] grant number [17 QNZD005]. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable.