# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 1 Introduction  
In South Korea traveling by train is one of the most convenient modes of transportation. A study showed that the railroads in South Korea cover a length of 3688 Km. A railroad commonly consists of steel rail, ballast bed, railway sleeper, railway fastener, and other parts of a railway track. Among all the components, the sleeper is a crucial component of a railway track. The principal roles of a railway sleeper include distributing loads of the trains from the steel rails to the ballast bed, reducing track movement, and stabilizing the track gauge during train travel, all of which assure safe travel for the train passengers. Timber sleepers, concrete sleepers, and to a lesser extent, steel sleepers reinforce polymeric sleepers that are commonly used on railway tracks. However, literature shows that concrete sleepers are mostly used (60-80%) for several advantages in a railway network [1]. These concrete sleepers can be damaged in the form of cracks due to rain erosion, exposure to the sun, the reaction of salts in the earth with concrete sleepers, long-term navigation, an overload of trains, and so on. These cracks can introduce dangerous situations and can cause deadly accidents based on the daily loads of traffic and the severity of the cracks. So, detecting cracks early is crucial for inspecting and assessing the usability of concrete sleepers. Over the years, manual inspection has been a common and traditional method to detect cracks in railway sleepers. However, physical assessment has blind spots, and it lacks accuracy. Furthermore, this approach is time-consuming, labor-intensive, and costly. Inspectors rely solely on their human vision while traversing railway tracks to detect cracks. Besides these,the inspection performance can be varied from time to time based on the experience level of the inspector. Replacing the manual inspection with vision-based technologies can overcome these problems, and the sleeper cracks can be detected effectively. Recently, many advances have been made in the computer vision field. And, the image-based techniques have already shown mesmerizing performance in other concrete structures like concrete pavement [2], buildings [3], tunnels [4], concrete bridges [5], etc. The image processing techniques make the concrete crack detection task fast and accurate. Therefore, utilizing the image processing methods will ensure automated sleeper crack detection and alleviate the personnel's tedious and repetitive tasks. There are primarily two types of processing methods. The first method is to extract the features, remove the noises, and then implement classification. The image features are extracted by different types of feature extraction methods like wavelet transformation [6], Percolation methods [7], Otsu's method [8], the morphological approach [9], etc. After extracting the features, classification algorithms are employed to classify the cracks in the images. Traditional image processing methods need to select the most relevant feature extraction approach. Even though numerous image feature extraction methods exist, there is no global feature extraction strategy to cope with images in various situations. As a result, a significant series of experiments or even novel feature extraction feature techniques must be devised to identify a suitable feature extraction approach for a specific situation. However, these methods fail to deal with images having complex patterns, extreme noises, and intensity inhomogeneity. The other approach to image processing is utilizing the Deep Learning (DL) technology, especially the Convolutional Neural Network (CNN). CNN utilizes many hidden Neural Network (NN) layers to extract the underlying features automatically and classify the images. With the development of the AlexNet [10] using CNN in 2012, the advancement curve observed a huge surge in the field of computer vision. Following this, other CNN models with various depths have been designed.