# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 2 Related Work
### Crack Detection on Railway Sleepers  
Though there are many research works for detecting cracks on various concrete structures, there are not many equivalent works to detect cracks on railway sleepers. Rather, vision-based techniques are employed to a lower extent for solving some other similar types of problems in railway industries. For example, [PERSON] et al. detected cracks on railway tracks using a vision-based technique by including edge detection methodology [47]. [PERSON] et al. presented a method by combining local binary features and an SVM classifier for detecting defective fasteners in railway tracks [48]. [PERSON] et al. employed a method based on a canny edge extractor and hough transformation to monitor the condition of the railway components [49].  
Crack monitoring in rail tracks can be viewed from various perspectives, each with its own set of objectives. [PERSON] et al. presented a machine vision system for detecting cracks on railway tracks. The authors first extracted the features using the Gabor transform and passed those features to a neural network classifier. They achieved an overall accuracy of 94.9% during detecting the cracks [50]. [PERSON] et al. detected cracks on railway tracks using two steps. First, the authors found the tracks using the features of the hue channel, and later, they performed contour-based surface profiling to classify the defects [51]. [PERSON] et al. detected cracks not only on wooden sleepers, but also on concrete sleepers by utilizing a vision-based technique. However, as the authors developed the system based on a binary thresholding technique, it may lose robustness during environmental and illumination changes [52]. [PERSON] et al. proposed a crack detection method for railway sleepers using the template matching technique first to detect the concrete sleepers. Then, they used linear SVM and Radial-basis Function SVM (RBF-SVM) to classify the crack types on the sleepers. Numerical results showed that RBF-SV performed better [53]. [PERSON] et al. proposed an advanced method using the Adaboost algorithm for detecting cracks on railway sleepers. Their algorithm identified the cracks with an identification rate of more than 90% [54]. [PERSON] et al. proposed a two-stage algorithm for detecting cracks on concrete railway sleepers with less computation time. First, they used an edge detection technique called neighborhood range algorithm for selecting crack areas and then utilized CNN on top of it to successfully classify the crack types [55]. [PERSON] et al. presented a novel framework named CF-NET based on the RetinaNet object detection framework to detect cracks with bounding boxes on railway sleepers [56]. [PERSON] et al. proposed a modified version of Single Shot Detector for detecting cracks on railway sleepers. The authors compared between two images and detected deformed regions to find the cracks [57].  
The literature shows that already there are some remarkable DL-based research works for detecting cracks in different concrete structures. However, most crack detection systems for railway sleepers rely on traditional image processing techniques. Though there are a few CNN-based works, they cannot detect cracks at a pixel level. To the best of the authors' knowledge, no prior research work has utilized a DL model based on encoders and decoders to identify pixel-level cracks on railway sleepers. So, for the first time, in this work, we adopt U-net and propose a modified U-net architecture named Dense U-net for detecting railway sleeper cracks at a pixel level. Moreover, we also calculate a few parameters of the cracks (e.g., length, maximum width, area, density).