# SCCDNet: A Pixel-Level Crack Segmentation Network
## 2 Methods  
### Model Architecture  
The structure of the pixel-level SCCDNet is shown in Figure 1. The model uses the classic Encoder-Decoder [28] structure, in which the Encoder learns the features of images at different scales, and the Decoder obtains predicted images with the same resolution as the input image by combining the feature map in the Encoder module.  
As shown in Figure 1, the left dotted box contains the structure of the Encoder. We used 13 convolutional layers pretrained in the VGG-16 network as the main structure of the Encoder. In order to reduce the amount of model parameters without affecting the performance of the model, we used depthwise separable convolution [33] instead of traditional convolution. Each depthwise separable convolution is composed of a Depth-wise (DW) convolution and a Point-wise (PW) convolution, which can reduce the amount of calculation to the original \(1/N+1/D_{K}^{2}\) without reducing the performance of the model, where N is the number of channels of the output feature map, and \(D_{K}\) is the size of the convolution kernel. In this model, the size of the convolution kernel of all DW convolutions is \(3\times 3\), and that of all PW convolutions is \(1\times 1\). In order to ensure that the dimension of the output  
Figure 1: Structure of SCCDNet. SCCDNet consists of three parts: Encoder module, Decoder module, and SSE module. See text for details.  
feature map remains unchanged, we set padding = 1 in DW convolutions. In addition, in order to enhance the generalization ability of the crack segmentation model, considering the need to segment cracks of different sizes, we used 5 downsampling operations with a size of 2 \(\times\) 2 to reduce the resolution of the feature maps and learn the characteristics of cracks on different scales.  
The right dotted box contains the structure of the Decoder. DenseNet [34] proved that establishing short connections between the layers of the network will strengthen feature propagation, encourage feature reuse, alleviate gradient disappearance, and make training more efficient. Inspired by this, we designed a densely connected structure in the Decoder module, reducing the number of channels of each feature map, and improving the performance of the model. The dash-dotted lines indicate the omitted operations. The detailed structure of the Decoder module is detailed in Section 2.3.  
Compared to U-Net [27], which directly uses the feature map of the Encoder as the input of the Decoder, [PERSON] et al. [35] verified that introducing the Attention Gate (AG) model into the connection between the Encoder and the Decoder, allowing the model to automatically focus on targets of different shapes and sizes, while emphasizing the area of interest while suppressing irrelevant area. Therefore, we used the SSE module to connect the Encoder module and the Decoder module. The SSE module plays the role of AG model, and the structure of it is detailed in Section 2.2. In order to further reduce the amounts of parameters, we compressed the number of channels before using the feature map output by the Encoder module as the input of the SSE module. In addition, all activation functions used in the model are the rectified linear unit (ReLU) [36]. The codes of our model are available at [[https://github.com/543630836/SCCDNet_crack](https://github.com/543630836/SCCDNet_crack)]([https://github.com/543630836/SCCDNet_crack](https://github.com/543630836/SCCDNet_crack)), accessed on 29 May 2021.