## 1 Introduction  
Cracks on the surface of roads account for a significant proportion of road defects. Therefore, how to efficiently and safely detect and classify highway cracks is of great significance for maintaining highway safety. We conducted on-site visits and surveys of relevant personnel from the highway management department, and we learned that the current method of road inspection still mainly relies on manual detection. A team of four to five people working a full day can inspect approximately 3 km, and the inspection process also requires relevant road sections to be closed. This demonstrates that the manual inspection method is inefficient, laborious, and has a high rate of missed inspections and misclassification of cracks, which can result in serious consequences. If cracks are not detected and repaired in a timely manner, they can worsen and lead to more severe damage, such as potholes or even complete pavement failure. This can result in safety hazards for drivers as well as increased maintenance costs for repairing the damage. In addition, misclassification of cracks can lead to incorrect maintenance decisions, such as allocating resources to repair non-existent cracks or neglecting actual cracks that require attention. This can result in wasted resources and increased risk of accidents. The methods to solve the problems of road crack detection and classification include timely detection and repair of cracks, improvement of existing mainstream crack detection networks, creation of algorithms suitable for batch classification of cracks, and establishment of efficient crack detection and classification systems, among others. Over the past few decades, the automatic detection of cracks using ultrasonic detection has been proposed [1] to solve the problems associated with manual visual inspection classification. However, ultrasonic testing requires specialized technicians for operation and may be affected by the surface conditions of the object being tested. Moreover, ultrasonic testing equipment is expensive and requires calibration within a certain range to ensure accuracy. Later, crack detection methods based on images and image processing have gradually appeared [2; 3]. However, image-based detection requires high-quality image data and processing of large amounts of data. Due to the complexity of the road surface, it is difficult to accurately detect and classify all types of cracks, especially small and shallow cracks, which may result in misidentification or missed detections. Then, with the development of artificial intelligence, various network models for crack detection appeared [4; 5; 6]. However, none of these networks can be applied to the detection of cracks in unmanned aerial vehicle (UAV) remote sensing images. Traditional machine learning algorithms, such as support vector machine and extreme learning machine, have also been used for crack classification [7]. The cracks are classified by calculating the horizontal projection, vertical projection, Euler number, and other metrics on a crack graph [8]. This method cannot classify cases where there are multiple cracks in the graph, and it cannot be applied to complex scenarios and batch processing. To address the problem of low efficiency in manual and vehicle-borne inspection, this paper makes a bold attempt at data acquisition by using a professional mapping drone to collect road crack data through aerial photography at 80 m altitude. A single UAV sortie can shoot a 12 km road section, which takes about 30 min and is thus tens of times more efficient than the manual approach and has a higher safety factor. However, the flying altitude of the UAV is too high, which leads to a lot of environmental noise and unclear cracks in the captured image. Moreover, the shooting effect is affected by the lighting conditions. All of these issues make it challenging to detect cracks in the subsequent image analysis.