## 1 Introduction  
Cracks are the most common form of early pavement defects, and in the process of road use, because of driving load and the effects of wind, rain, ice and snow, freezing/ thawing, sun and other natural factors, cracks and other damages will inevitably arise. However, as of 2019, the total mileage of the U.S. highway network reached 6,853,024 km, of which about 63% has been paved. By 2020, the total mileage of China's highway network reached 5,198,000 km, of which 95% has been paved [1, 2]. For practical engineering applications, the pavement management department needs to examine a large number of road miles [3]. At present, some nondestructive testing techniques have made some progress in pavement crack detection. For example, GPR technology is used to study the width and extension depth of cracks through numerical simulation analysis, and even the extension depths or hidden cracks can be located [4]. Ultrasonics are also applicable to the detection and characterization of cracks at the millimeter level; the ultrasonic sizing procedure presented in [5] combines time-domain and frequency-domain near-field analysis to push surface crack sizing down to crack initiation size. Optical fluorescent microscopy technology is also used to identify and quantify concrete cracks. The use of a resin impregnation technique in combination with optical fluorescence microscopy and a computer image analysis technique provided a quantitative determination of the crack system in [6]. However, due to the high cost of detection time and high technical requirements for relevant operators, the above methods cannot be adapted to the current huge base of pavement crack detection. Cracks are characterized by various shapes, uneven ratios of length to width, and irregular spatial trends. The problem of accurately extracting and segmenting cracks is still a challenge for human beings. With the rapid development of computer vision and digital image processing technology, automatic path recognition and segmentation technologies are gradually becoming mainstream. Ways of improving the performance of image recognition and segmentation systems for pavement cracks are also a focus of research around the world. In early studies, there were three conventional methods for pavement crack segmentation:  
1. Extracting the crack features with thresholding. [PERSON] [7] determined the approximate location of cracks using straight-square threshold segmentation that was just suitable for wide cracks. [PERSON] [8] improved the segmentation ability of the algorithm based on automatic threshold segmentation and connected component methods but found it difficult to identify cracks when the contrast between cracks and pavement was low. 2. Extracting the crack features with edge detection. [PERSON] et al. [9] made use of multi-scale image fusion to detect pavement cracks, but their method was sensitive to the environment and crack texture distribution. [PERSON] [10] combined a modified canny edge detector with edge filtering to enhance crack features at the preprocessing stage. However, this method was sensitive to shadow and noise, and had a limited scope of application. 3. Extracting the crack features with machine-learning-based approaches. [PERSON] [11] used the random structured forest method to find the mapping relationship between cracks and structural markers. [PERSON] [12] employed a discrete wavelet transform for initially decomposing the road image into several sub-images, followed by classifying the images into cracked and non-cracked images using SVM [13]. Although these methods improve the detection accuracy compared with traditional image processing techniques, they cannot achieve satisfactory results in practical applications because of the limited performance of their feature extraction. In practice, the pavement materials used in different road sections often vary, meaning that the depth, shape, continuity, and even contrast of cracks are significantly diverse.