# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 2 Related Works
### Deep-Learning-Based Methods  
Deep-learning-based crack segmentation methods mostly use semantic segmentation models. In 2015, [PERSON] et al. [18] achieved the first end-to-end segmentation of natural images using fully convolutional neural (FCN) networks, which have thus become the most classical network model in the field of semantic segmentation. [PERSON] et al. [25] used a FCN backbone and a deeply supervised approach to upscale and fuse the feature maps from all levels of the backbone, and then applied a guided filter to fuse all feature maps as well as the side outputs to create a segmentation output. [PERSON] et al. [38] used dilated convolution with a different dilation rate in the last four layers of the FCN to expand the receptive field without changing the feature map scale, and used skip connections to deliver shallow feature information, assisting the decoder in generating segmentation results. However, the methods based on FCN networks still suffer from information loss when up-sampling low-resolution feature maps generated in the deep layer of the feature extraction network. To solve the problem, symmetric encoder-decoder-based network structures, such as SegNet [26] and U-Net [19], have been proposed. In particular, U-Net has had a profound impact on many subsequent studies due to its pioneering concept and excellent performance, and a series of semantic segmentation models such as UNet++ [39] and Unet 3+ [29] have been derived on its basis. Since the detailed spatial information of cracks can be more effectively restored, many recent studies of crack segmentation are based on the SegNet and U-Net structures. [PERSON] et al. [40] introduced a spatial attention mechanism and a channel attention mechanism in SegNet and used spatial pyramidal pooling to capture crack features from different scales. [PERSON] et al. [3] pair-wisely fused the feature maps generated in the encoder and decoder network at the same scale, and generated segmentation results by extracting features from the fused feature maps at multiple scales using a multi-scale fusion component. [PERSON] et al. [27] replaced the plain convolutional neural network of the encoder of U-Net with a residual network and added spatial and channel compression excitation modules to the decoder. Based on U-Net, [PERSON] et al. [28] designed a skip-level round-trip sampling structure, in which the deep feature maps of the encoder network were up-sampled and aggregated with some shallow feature maps, and then down-sampled and fed into the decoder network. These up- and down-sampling actions enhanced the network's memory of transmitting low-level features in the shallow layer, helping the network to pay attention to the distinction between the cracks and the background. [PERSON] et al. [30] proposed PSPNet, which applies special pyramid pooling to the semantic segmentation task and extracts multi-scale contextual information. Some other studies also explored spatial pyramid pooling, such as the DeepLab series [41; 42; 43], although the difference is that DeepLabs use a dilated convolution rather than pooling to obtain contextual information at multiple scales. [PERSON] et al. [44] adopted and enhanced DeepLabv3+, in which a multi-attention module was introduced to dynamically adjust the weights of different feature maps for pavement crack image segmentation. [PERSON] et al. [45] proposed OCR-Net, which uses object contextual feature representation for contextual information extraction based on object regions, thus explicitly enhancing object information and achieving good results on several mainstream semantic segmentation databases. [PERSON] et al. [46] explored an exemplar-based regime which provides a nonparametric segmentation framework based on non-learnable prototypes, where several typical points in the embedding space are selected for class prototypical representation, and distance to the prototypes determines how a pixel sample is classified.