## 2 Related Works  
In this section, we present a brief introduction to related works on crack detection. Earlier research about pavement detection was usually based on digital signal processing methods. For example, [PERSON] [9] proposed a pavement detection method, which can be divided into following steps: Firstly, a new vehicle for collecting digital highway data was procured and four cameras were mounted at the rear of the vehicle to collect pavement surface images across a 4 m wide pavement. Secondly, the Direct Linear Transformation (DLT) method was used to correct camera distortion. Finally, the algorithm-based signal processing method was used to classify the crack area. [PERSON] [10] proposed a probability-map-based crack detection method. In his method, firstly, a geodesic shadow-removal algorithm is used to remove the pavement shadows. Then, a crack probability map is used to enhance the connection between the crack fragments. Finally, a set of crack seeds from the crack probability map are represented by a graph model and used to generate the final result. These methods rely heavily on the manual design of digital filters and special algorithms, as well as on manual experience. They also have limited generalization and can only achieve good results on specific datasets. Thus, researchers began looking for a new approach. Deep learning technology has been gradually developed since 2012. This technology can automatically extract high-level features from crack images, so there is no need to manually design features. Crack detection based on deep learning mainly focuses on semantic segmentation technology, which is mainly divided into the following categories:  
Specialized CNN-based model: [PERSON] [11] proposed a two-stage convolutional neural network (CNN) for road crack detection and segmentation in images at the pixel level. In his design, the first stage is to remove noise or artifacts in cracks; then, the second stage is to learn the contextual information of cracks in all detected areas. [PERSON] [12] proposed a semantic segmentation model with an auto-encoder structure. In addition, he proposed a CNN-based image preprocessing model to deal with lighting conditions. However, these models have some common shortcomings: Firstly, the obtained results are still not fine enough, and the up-sampling results are still fuzzy and insensitive to the details in the image. Secondly, each pixel is directly classified without fully considering the relationship between pixels. Spatial feature fusion is neglected, resulting in a lack of spatial consistency. DeepLab-based model: To solve the problems in these Specialized-CNN-based model. [PERSON] [13] proposed a DeepLabv3+ model for crack detection. In his design, DeepLab v3+ uses the layer-fusing policy to process spatial feature fusion. The use of DeepLab v3+ with different backbones is compared, and the results show that DeepLab v3+ with the ResNet101 backbone could achieve the highest accuracy. Furthermore, [PERSON] [7] introduced a densely connected atrous spatial pyramid pooling module to DeepLabv3+; thus, the network could obtain denser pixel sampling, which could solve the fuzzy segmentation problem. However, these DeepLab-based models have many parameters and require a large amount of calculation, which consumes a lot of training time. Moreover, this kind of model does not adopt the symmetric encoder-decoder structure, which could distort the detail of crack images. UNet-based model: Based on the models above, researchers decided to improve the drawbacks of DeepLab-based models. Thus, they designed a segmentation model with a symmetric encoder-decoder structure named Unet. [PERSON] [14] proposed a UNet model. In his work, he desired to optimize the crack segmentation process through the implementation of a modified U-Net model-based algorithm. Experiments show that the U-Net based models could obtain a higher accuracy than traditional CNN models. In addition, [PERSON] [15] presented an extended version of the U-Net framework, named MSK-UNet.