# SCCDNet: A Pixel-Level Crack Segmentation Network
## 4 Conclusions  
In this paper, we design an end-to-end crack segmentation network based on CNNs, which can detect the crack image in pixel-level. SCCDNet is composed of the Encoder module using 13 convolution layers pretrained in VGG-16 network, the Decoder module with dense connection structure, and the SSE module connecting the Encoder and the Decoder. In order to further reduce the complexity of the model, we replace all convolutions with depthwise separable convolutions. In practical applications, the aim of crack detection is to detect different forms of cracks in various environments. However, most of the existing crack detection models detect a single form of cracks in a dataset collected in a single environment, and the dataset is small. Therefore, we established a dataset with 7169 labeled images (crack images with different textures collected in different environments) and conducted training and testing on this dataset. We will publish the code and dataset of the model used in this paper, so as to support other researchers to compare with our model. We prove the performance of our model by comparing SCCDNet with other popular models. Results show that SCCD-D32 is the best model in the SCCDNet series, and its performance in crack segmentation accuracy and model parameters is better than other models. Although the overall performance of the SCCD-D32 model is excellent, its m-Precision can only reach 0.7294, which is much lower than the 0.8296 of m-Recall, which proves that the ability to resist error detection is weaker than the ability to resist missed detection. Therefore, we will continue to conduct in-depth research on improving the model's ability to resist error detection.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **m-Precision** & **m-Recall** & **m-F-Score** & **m-Dice** & **m-IoU** & **FLOPs/G** & **Parameters/M** \\ \hline Normal Conv & 0.7281 & 0.8112 & 0.7674 & 0.7485 & 0.6310 & 83.786 & 39.499 \\
**D-S Conv** & **0.7294** & **0.8296** & **0.7763** & **0.7541** & **0.6402** & **65.004** & **31.705** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of different convolution strategies. The best average scores are in bold.  
**Author Contributions:** Conceptualization, H.L. and Z.Y.; methodology, H.L.; software, H.L. and J.L.; validation, H.L. and Y.W.; formal analysis, H.C. and J.L.; writing--original draft preparation, H.L.; writing--review and editing, Y.W. and Z.Y.; supervision, H.C. and X.C.; project administration, X.C. and K.C.; funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research was funded by the Tianjin Municipal Transportation Commission Science and Technology Development Plan Project (2019C-05).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available in [SCDNet_crack] at [[[https://github.com/543630836/SCDNet_crack](https://github.com/543630836/SCDNet_crack)]([https://github.com/543630836/SCDNet_crack](https://github.com/543630836/SCDNet_crack))], accessed on 29 May 2021.  
**Conflicts of Interest:** The authors declare no conflict of interest.