## 1 Introduction  
As urbanization increases, highway networks have been developed to meet challenges and demands in transportation. As highways comprise part of the critical infrastructure for public transportation, promoting advanced analysis and assessment technology for highway systems is an important part of an intelligent transportation system (ITS). In the past decade, an increasing number of highways have been damaged due to the poor environment, car overload, material aging, and so on [1]. Pavement distress detection, identification and classification are important steps in a pavement management system (PMS). It helps the agency to determine the appropriate rehabilitation techniques to be performed on the pavement [2]. Generally, cracks are the earliest signs of pavement distress. The continuous propagation of cracks without proper treatment in the early stages of damage will result in high maintenance costs and severe consequences. Therefore, detecting cracks and repairing them quickly are essential tasks for highway maintenance departments. Normally, highway maintenance workers use sealants to repair cracks. However, when the pavement structure has been damaged, cracks are often more likely to develop around the sealed crack, so it is also necessary to detect sealed cracks. The traditional routine of highway pavement distress inspection relies on manual on-site surveys, which are labor-intensive and time-consuming. In addition, highways are dangerous working environments for road inspection personnel [3]. It is therefore necessary to develop automatic and efficient methods of detecting highway pavement cracks and sealed cracks. At present, special road condition inspection devices have been extensively studied. Non-contact devices such as thermal imaging sensors [4] and ground-penetrating radar [5] have been utilized to detect cracks and take advantage of the differences in the signals returned from normal and damaged pavements. Embedded fiberoptic sensors [6] are also an emerging technology used to detect pavement distress. Although the tools mentioned above can accurately locate pavement distress, their high cost and low efficiency are critical drawbacks in real-world scenarios, preventing them from being applied to a wider range of situations. Image acquisition methods based on charge-coupled devices (CCDs) and complementary metal oxide semiconductor (CMOS) sensors are prevalent because of their efficiency and low cost. However, the detection of pavement distress using image processing is still a very challenging task, especially for asphalt pavements. The reasons for this can be summarized as follows:  
* The irregularity of the pavement surface [3];
* Cracks are characterized by low-level features that can be easily confused with the background texture and other objects on the pavement [7];
* The influence of external conditions such as illumination and camera jitter on the image quality. Image processing methods such as transformation, enhancement, and segmentation have gained the attention of researchers in the field of pavement assessment, but these methods are empirical [3], i.e., they require constant adjustment of the parameters to achieve an optimum result. Traditional machine learning methods, such as random forest [8; 9] and AdaBoost [10], have also been used to detect cracks in pavements. The problem is that such methods can only obtain low-level image information and cannot extract high-level semantic information, which has a significant impact on the robustness of the algorithms. With the increase of parallel computing power and the development of deep learning, data-driven methods are widely used in PMS in various countries [11; 12]. Convolutional neural network (CNN) [13] is one of the well-known data-driven methods that can automatically learn high-level information from large amounts of data through a multilayered artificial neural network (ANN), which can be used to classify images or detect objects.