## 1 Introduction
Hence, traditional edge-detection algorithms find it difficult to achieve ideal results on fractures in diverse environments [14; 15]. With the development of deep learning technology, the ability of computer systems to recognize and process images has been greatly improved because of the use of DCNNs and some image techniques using deep learning [14; 16; 17; 18] are developed to detect cracks. The FCN [19] network is the first end-to-end semantic segmentation network that uses a fully convolutional neural network (FCN) architecture, and performs dense execution based on \(1\times 1\) convolution to reduce parameters and improve speed. The experiments of [PERSON] et al. [20] show that the FCN network can achieve the pixel-level classification of concrete surface images of different types of cracks with high accuracy. However, some studies show that the upsampling performed in different steps can cause the FCN to lose some detailed information, resulting in inaccurate results for images with small cracks and complex patterns [21]. To overcome some limitations of FCN architectures, various types of neural network architectures have been proposed. The current mainstream segmentation algorithms are divided into three categories:  
1. Structure of feature concatenate based on skip connections: the U-Net [22] model is an encoding and decoding segmentation network with a symmetric structure. [PERSON] et al. [23] showed through experimental results that under small data training, U-Net can also achieve high segmentation performance with an accuracy of 0.9 in crack detection. U-Net++ [24] is an improvement of the U-Net model that uses dense concatenation in the upsampling phase with low inference speed. The Segnet [25] network generates the pooled position indexes to combine high-level abstract features with shallow-level detailed features to improve the performance of edge detection. Deepcrack [26] uses a multiscale feature fusion method in the upsampling phase to improve accuracy by fusing the feature maps obtained through the decoder and the encoder. In the experiment of [PERSON] [26] et al., Deepcrack showed a better feature extraction effect for thin cracks, with its AP reaching 0.9. 2. Structure based on multi-scale feature fusion: PSPnet [27] uses the pyramid pooling module (PSP) to aggregate the context information of different regions, thereby improving the ability to obtain global information. DeepLabV3+ [28] is one of the DeepLab [28; 29; 30; 31] series of networks, which improves the perceptual field by using dilated convolution and fuses the feature information under different perceptual domains by using spatial pyramid-pooling (SPP) [32]. However, some experiments [33] found that Deeplabv3+ still has some accuracy limitations for target edge extraction, and there is still room for improvement in the detection effect of small cracks. 3. Structure based on multilateral feature fusion: a bilateral segmentation network that fuses spatial path (SP) and context path (CP) is proposed in [29], named BiseNet. SP and CP are respectively used to encode rich spatial information and to encode high-level contextual information through large receptive fields, which complement each other to reduce feature loss and achieve higher performance. Although there are advantages to U-based models in crack detection that can be trained with small datasets and higher accuracy, their complete U-shaped structure slows down inference speed [34], making them difficult to achieve real-time detection at high-resolution images. In the current U-Net variants [35; 36; 37], the coding structure of only a single type of feature extractor is often used, ignoring the impact of the way the feature extractors are modeled at different levels on the performance of the DCNN models. In addition, the DCNN model generates redundancy in the feature map when the convolution operation is performed, and each location independently stores its own feature description characteristics, while ignoring the public relationships between adjacent locations [38].