# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 2 Related Work  
With the development of sensing technology, it is widely used in concrete damage detection. [PERSON] et al. [14] used non-destructive electromagnetic (EM) device to detect cracks. More specifically, [PERSON] et al. [14] used ground penetrating radar (GPR) with 2 GHz high frequency bipolar antenna to scan reinforced concrete beams to detect cracks. [PERSON] et al. [15] proposed a mounted Smart Aggregate (SA)-based approach for real-time health monitoring of existing reinforced concrete beams, which effectively assessed the cracking state of reinforced concrete beams according to the peak power spectral density and damage index obtained at multiple sensor locations. [PERSON] et al. [16] proposed a non-destructive detection method based on remote sensing technology to evaluate the data acquired by the Terrestrial Laser Scanner (TLS) to detect cracks and mass loss in concrete.  
As computer vision has made remarkable achievements in various fields, such as object tracking [17; 18; 19; 20], remote sensing image processing [21], and style transfer [22], more and more researchers focus on the research of concrete damage detection based on computer vision. The concrete damage detection methods based on computer vision is mainly divided into traditional image processing-based methods and deep convolutional neural network-based methods.  
As for those methods based on traditional image processing, low-level visual features such as edge, shape and structure information are extracted, and then the features are used for detecting concrete damages [23; 24; 25]. [PERSON] et al. [23] developed a method for searching and measuring cracks in RGB images of concrete components. The algorithm uses a couple of digital image processing tools to detect crack width and direction. Generally, concrete images only contain damaged and non-damaged parts. Non-damaged parts are the major mode of the gray-scale histogram. Therefore [PERSON] et al. [24] detects concrete cracks by handling histogram threshold to extract regions of interest from the images. Also using the gray information, [PERSON] [1] proposed a gray intensity adjustment method, which improved the detection results of cracks by adjusting gray intensity. In order to alleviate the influence of illumination and complex background, and reduce the situation of fracture detection results, [PERSON] et al. [25] proposed an improved image crack detection algorithm based on the percolation model. Firstly, the skeleton extraction algorithm based on directional chain code was used to extract the crack skeleton, and then the part of the fractured cracks is reconnected by the region expansion-based algorithm. [PERSON] et al. [2] proposed a crack detection framework based on multi-scale enhancement and visual features to improve the robustness of crack detection in complex background. Most of the traditional image processing-based methods focus on crack detection under non-complex conditions. However, the real concrete images have a lot of noise and complex background. Therefore, it is a challenging task for this kind of algorithm to separate the crack from the background.  
Because of the great success of deep convolutional neural networks (DCNN) [26; 27; 28; 29; 30; 31; 32; 33], more and more concrete damage detection algorithms based on DCNN have been proposed [34]. These algorithms can be summarized into three categories: image patch classification method, boundary box regression method and semantic segmentation method.