# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 5 Discussion and Conclusions  
In this research work, we presented a modified U-net network named Dense-Unet for detecting cracks on the concrete sleepers of a railroad. At this point, many DL-based methods have already been developed to detect cracks at a pixel level on different concrete structures. However, before our research, no previous research detected cracks at pixel levels on the concrete sleepers of a railroad. To modify the original U-net structure, several short connections were established between each of the encoder and decoder blocks, which extract denser and multi-scale necessary features and obtain and pass denser pixel information. Both the quantitative and qualitative results showed the proposed algorithm performed better than the conventional U-net model when predicting cracks on the concrete sleepers. The overall F1-score reached 86.56%, which was 2.15% greater than the original U-net model. Furthermore, we analyzed the severity of cracks in the predicted images by calculating the length, maximum width, area, and ratio of the cracks. Compared with the previous works in the field of crack detection in railway sleepers, the advantage of our method is it can detect the cracks at the pixel level, whereas the previous DL-based works can only detect the existence of cracks from the images. Our method can detect the existence of cracks from the images as well as localize the cracks in the image. Besides these, our method can extract the shape of the cracks in an image and detect the pixels containing the cracks, which helps find out the size of the cracks and the severity of the cracks. In short, our work advances the field from the present state of knowledge by implementing a segmentation technique rather than using classification techniques that open a window for having the idea of crack locations, crack shapes, and the severity of cracks in railway sleepers. The disadvantage of our method is the DL-based supervised model is always in need of labeled data, which is difficult and time-consuming to annotate. Moreover, during the creation of the dataset, we collected the images from an indoor environment. Our dataset does not cover the images of concrete sleepers of different locations, of different time frames, (e.g., morning, noon, evening, night), and different illumination conditions. As a consequence, our developed method may struggle during practical real-time detection. As in a real environment, there might be extreme sunshine, and dark shadow, which can vary the brightness level of the pictures, and the sleepers may have different textures. Hence, our trained model can find it difficult to detect railway sleeper cracks during practical application. So, in the future, domain-adaptive self-supervised learning techniques can be adopted to detect cracks in railway sleepers to solve the issue of annotated data as well as the problem of domain shift. One more limitation of our method for severity analysis is that if a crack object has multiple horizontally parallel branches, our model overestimates the maximum width and overlooks the maximum width location. In our future work, we plan to develop a more accurate segmentation model as well as a regression-based Deep Learning technique for detecting crack sizes, and severity without any postprocessing techniques.  
Conceptualization, M.A.-M.K. and S.-H.K.; methodology, M.A.-M.K., A.-A.N. and S.-H.K.; software, M.A.-M.K. and S.-H.K.; validation, S.-H.K., A.-A.N. and M.A.-M.K.; writing--original draft preparation, M.A.-M.K., S.-H.K. and A.-A.N.; writing--review and editing, S.-H.K., A.-A.N. and M.A.-M.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1A6A1A03012812).  
Not applicable.