## 1 Introduction  
Highway pavements are affected by many factors such as the natural environment, load conditions, structural combinations, materials, construction techniques, and technical levels, which can produce various types of distress. With the construction of highways, pavement maintenance has begun increasing sharply. Accurate pavement distress detection results can provide reliable and effective technical support for pavement maintenance management decision making, improve highway pavement service performance, and reduce traffic accidents. However, traditional manual detection methods are often affected by subjective judgment in detecting highway pavement distress. There were considerable errors and low detection efficiencies. Therefore, automatic distress recognition and feature measurement of collected pavement images are the mainstream means of pavement detection. The adoption of information management technology is an inevitable way to improve the level of highway maintenance management and realize efficient and orderly organization and management. For example, for common cracks on the highway, the development of an effective pavement crack identification algorithm can evaluate the pavement condition in advance and provide the basic data for maintenance decision making for the highway maintenance management department. The commonly used equipment for collecting pavement crack information include digital cameras, depth cameras, and lasers. Many researchers have studied pavement performance by using the images taken by digital cameras. At this stage, it has been applied to pavement crack detection [1], asphalt mixture crack detection [2], and concrete elements deformation tests [3]. Many researchers have recently begun to apply depth imaging technology to pavement detection engineering [4; 5; 6]. Unlike the traditional 2D camera, the depth camera can obtain depth information and provide the color image details in the 2D camera [7]. In addition, laser scanning is often used to detect pavement damage [8; 9]. Laser scanning technology was used to extract cracks in concrete [10]. Although the depth camera and laser scanner can extract the three-dimensional information of the pavement and more accurately identify the distresses. The use of these two devices is limited due to the high purchase cost of the equipment, the complex post-processing process of 3D data, and inconvenient daily maintenance. Pavement cracks usually appear as curved configurations with different widths in an image. They can be characterized by the edge detection and image segmentation methods in computer vision. In the ideal case, for such deep cracks with good continuity and no other noise interference, the traditional method can efficiently segment the crack from the image. [PERSON] et al. [11]. proposed a new double-threshold algorithm to obtain detailed information on the crack number and width. [PERSON] et al. [12]. proposed a triple-threshold pavement crack detection method using a random structured forest. However, in an actual detection task, different types of pavement types, shadows, and foreign objects will lead to a decline in the detection accuracy of the traditional methods. In addition to the automatic threshold segmentation method, there are crack detection methods based on spatial filtering and wavelet analysis; however, they have some disadvantages such as high requirements for equipment, complex operation, and environmental impact [13; 14; 15; 16]. In recent years, convolutional neural networks (CNNs) have been proposed and applied to computer vision tasks such as image classification [17; 18; 19], target detection [20; 21; 22], and semantic segmentation [23; 24; 25]. Simultaneously, a CNN-based method has also been applied to pavement distress detection. [PERSON] et al. [26] demonstrated the performance of the traditional and intelligent methods based on CNN in the pavement crack detection task. The experimental results show that the CNN-based crack detection methods are promising alternatives to regular methods.