## 1 Introduction
[PERSON] et al. [27] developed a hybrid model by integrating the Yolo and U-Net models to classify pavement distresses and simultaneously quantify their severity. [PERSON] et al. [28]. proposed a method based on Deeplabv3+ and a pixel-level quantization algorithm for crack detection. [PERSON] et al. [29] The CNN composed segmentation and classification modules to extract pavement cracks and remove the elements interfering in the image. [PERSON] et al. [30] proposed a nearly automated detection model based on image processing and deep learning to detect defects in areas where concrete structures usually cannot enter. In summary, traditional methods based on digital image processing have been widely used in pavement damage detection and have laid a theoretical foundation for methods based on deep learning. Methods based on deep learning have strong potential, are more accurate and convenient than traditional methods, and will be the mainstream methods for detecting pavement distress in the future. In the pavement crack detection task, the semantic segmentation model can be used to calculate the area occupied by cracks. It predicts the cracks pixel-by-pixel and segments the cracks from the image. The existing neural network models perform very well in the defect detection task, similar to the pavement crack detection task. For example, the U-Net semantic segmentation network was applied to the defect detection task in the industry [31; 32; 33; 34]. Inspired by the above methods and the U-Net network structure, we herein improve the U-Net convolutional neural network and apply it to crack identification in complex pavement conditions. When measuring the characteristics of cracks, calculating the width is necessary. The width calculation is related to the edge line, and the edge detection algorithm is used to extract it. Classical edge detection algorithms in computer vision include the Roberts operator, Sobel operator, and Canny operator [35]. These classical algorithms have also been applied to crack detection tasks. [PERSON] et al. [36] designed a local adaptive algorithm for Otsu threshold segmentation and proposed an improved Sobel operator to extract crack edge lines. [PERSON] et al. [37] proposed an adaptive Canny edge detection algorithm that achieved good results in crack detection. In addition to these modified traditional algorithms, some edge detection algorithms based on deep learning are also present. Holistically nested edge detection (HED) [38] and side-output residual networks (SRN) [39] are two relatively new edge detection networks, and both adopt the method of deep supervision to improve the training effect. [PERSON] et al. [40] continued the idea of deep supervision and proposed a Deepcrack for crack detection in multiple scenes. Similar to [PERSON] et al. [41], by combining the two networks of HED and U-Net, we proposed an end-to-end method for coast and coastline detection. Traditional edge detection algorithms are easily disturbed by environmental factors. Especially in the pavement surface images, factors such as rough surfaces, vehicle shadows, water stains, and uneven lighting brightness affect the edge detection accuracy. In addition, the edge detection algorithm cannot recognize the meaning of objects inside and outside the edge line; however, the combination of semantic segmentation and edge detection results can solve this problem. Therefore, a fusion model is proposed to segment cracks and simultaneously identify crack edge lines. The model uses a U-Net structure for image segmentation. It continues the idea of deep supervision in the HED and SRN networks. As the model uses the side-output method for edge line detection, it is called a side-output U-Net (SoUNet). The remainder of this paper is organized as follows. The second section presents the proposed network model and the evaluation indicators in detail. The third section describes the collection and production of data and introduces the process and details of training.