# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 5 Conclusions  
The detection of concrete cracks has extremely important practical application value, which can greatly reduce human and material resources. In order to accurately obtain the location and shape of concrete cracks, we modeled crack detection as a semantic segmentation task. Considering the loss of details caused by down-sampling in the traditional concrete crack segmentation algorithm based on FCN, we designed dilated convolutions with different dilation rates and a multi-branch fusion strategy. Because Resnet-18 has fewer parameters and requires fewer computing resources, we used Resnet-18 as the basic network. After removing the last down-sampling of Resnet-18, we introduced dilated convolution with different dilation rates, and then fused the features of different branches. Experiments showed that the design of dilated convolution with different dilation rates and the multi-branch feature fusion strategy can alleviate the problem of detail loss to some extent. From the experimental results of FCN-Resnet-18 and FCN-Resnet-50, the segmentation performance did not improve with the increase of network layers. Because the dataset is relatively simple and lacks semantic label samples with complex backgrounds, the proposed model suffers from erroneous segmentation when dealing with images with complex backgrounds. In addition, our model still has a lot of room for improvement for details loss. Our future work will explore the construction of a complex dataset with pixel semantic labels.  
Conceptualization, J.Z. and C.L.; Methodology, C.L.; Software, C.L.; Validation, C.L.; J.Z., L.W., J.W., and X.-G.Y.; Formal Analysis, C.L.; Investigation, C.L.; Resources, L.W.; Data Curation, X.Y.; Writing--Original Draft Preparation, C.L.; Writing--Review and Editing, J.Z.; Visualization, C.L.; Supervision, J.Z.; Funding Acquisition, J.W.  
Figure 13: The segmentation results of each model. Row (**a**) shows the result of segmentation of crack with large area. Row (**b**) shows the result of segmentation of crack with smaller area. Row (**c**) shows the results of segmentation of crack with relatively complex background. Row (**d**) shows the segmentation results of segmentation of crack with bifurcation.  
**Funding:** This research was funded in part by the National Natural Science Foundation of China under Grant Nos. 61772454, 61811540410 and 61811530332, the Scientific Research Fund of Hunan Provincial Education Department under Grant No. 16A008, the Postgraduate Scientific Research Innovation Fund of Hunan Province under Grant No. CX2018B565, the \"Double First-class\" International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology under Grant No. 2019 JC34, and the Postgraduate Training Innovation Base Construction Project of Hunan Province under Grant No. 2017-451-30.  
**Acknowledgments:** We are grateful to [PERSON] who provide the dataset (Concrete Crack Images for Classification, [[http://dx.doi.org/10.17632/59](http://dx.doi.org/10.17632/59) wwdsg2 zt.1]([http://dx.doi.org/10.17632/59](http://dx.doi.org/10.17632/59) wwdsg2 zt.1)).  
**Conflicts of Interest:** The authors declare no conflict of interest.