# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 2 Related Work
### The Methods Based on Semantic Segmentation  
This kind of method classifies each pixel of the image, that is, whether a pixel is a crack or not. [PERSON] et al. [42] proposed a U-Net based concrete crack detection method, which has high efficiency and robustness, and can identify crack locations under various conditions (such as illumination, complex background, and width of cracks), [PERSON] et al. [43] proposed an efficient CNN-based architecture called CrackNet for automatic pavement crack detection on 3D asphalt surfaces. The model fundamentally ensures the perfect accuracy of pixels by using newly developed constant image width and height technology for all layers. [PERSON] et al. [44] proposed a crack detection method based on FCN, which uses a deep convolutional network named VGG [26] as an encoder. [PERSON] et al. [45] used FCN to solve the problem that different cracks cannot be identified and measured simultaneously at pixel level. More specifically, FCN is used to semantically identify and segment pixels with different scales. The segmentation result is represented by a single pixel width skeleton to quantitatively measure the morphological characteristics of cracks and provide valuable crack indicators. This kind of algorithm classifies every pixel of the image and can detect the location of cracks more accurately.  
According to the above mentioned, the crack positions obtained by image patch classification and bounding box regression methods are relatively rough. Compared with these algorithms, more accurate crack location information can be obtained by classifying pixels. Some work [46] has proved that FCN is an excellent deep learning framework. Compared with other deep learning algorithms, FCN is an end-to-end, pixel-to-pixel network, which is very efficient and suitable for semantic segmentation. Therefore, we design a concrete crack segmentation model based on FCN. Unlike the concrete crack semantic segmentation algorithm based on VGG, we utilize the Resnet-18 as the backbone network. On the one hand, the Resnet-18 has deeper network layer, which can theoretically extract features with more semantic information. On the other hand, the Resnet-18 has fewer parameters, reducing the overhead of computing resources. Down-sampling, an important part of most network, usually results in the loss of information. Considering the loss of information caused by down-sampling, we design dilated convolutions [11] and a multi-branch feature fusion strategy to alleviate this problem. Considering that most concrete crack datasets only have image-level labels and lack pixel-level labels, we expand the dataset [12], and create a dataset with semantic information at pixel level through manual labeling.  
In general, our contributions are as follows:  
(1) Considering the tradeoff between accuracy and computation, we select Resnet-18 as the backbone network. Our experiments on Resnet-18, Resnet-50 and VGG show that Resnet-18 has fewer parameters and requires fewer computing resources.  
(2) Dilated convolutions with different dilation rates and multi-branch feature fusion strategy are designed, which further optimizes the feature map extracted by Resnet and makes the model have better performance.  
(3) According to the actual application requirements, the concrete crack detection problem is modeled as a semantic segmentation task, which more accurately extracts the crack location and shape. The evaluation indicators of semantic segmentation algorithm are introduced, which reasonably evaluate the performance of crack detection.  
(4) We expand the dataset [12]. More specifically, we manually annotated 600 images at pixel level to create pixel semantic dataset. In our experiment, 400 samples are used as training set, the verification set and test set are both 100 samples.