## 1 Introduction  
The highway plays an important role in promoting the economic growth of the country, the improvement in people's quality of life, and the coordinated development of regions. Scientific and timely maintenance of the highway can ensure traffic safety, extend the service life of the highway, and provide users with a safer, more convenient, and more comfortable travel environment. In recent years, the total amount of highway infrastructure in China has increased significantly, so the efficient and accurate detection of hidden distresses in highways is a key problem that needs to be addressed urgently. So far, a variety of non-destructive testing (NDT) technologies have been applied to recognize hidden distresses in pavement, which can provide a scientific basis for highway maintenance, for example, ground penetrating radar (GPR) [1], X-ray [2], and ultrasonic damage detection [3], and so on. As a non-destructive geophysical method with high efficiency, high precision, and continuous sampling [4; 5; 6], GPR has become one of the mainstream detection methods for hidden distresses in highway. At present, the amount of data obtained by a vehicle-mounted GPR for highway hidden distress detection is relatively large. Manual interpretation relies heavily on manpower and practitioner experience, which is time-consuming and costly, and human error is inevitable. To avoid these problems, automatic interpretation or automatic identification of hidden distresses in highways has become a current research focus [7]. In 2012, AlexNet was proposed as the first convolutional neural network (CNN) to attract widespread attention. Since then, CNNs of different architectures have appeared successively, such as visual geometry group network (VGG), residual network (ResNet), MobileNet, etc. Due to the powerful feature extraction capability of CNNs, they have been widely used in distress classification and detection. Hidden distresses of highways can be roughly divided into two types: distresses with hyperbolic characteristics such as cracks and voids; distresses with non-hyperbolic features such as interlayer distress and structure loosening. At present, most studies mainly focus on the automatic recognition of distresses with hyperbolic features. For example, the recognition CNN and the location CNN were respectively designed to automatically identify and locate concealed cracks between the pavement surface and the base layer [8]. As a one-stage object detection framework, YOLO [9] has a high detection efficiency and has evolved into a series of versions. DarkNet was used as the backbone of YOLOv3, with four-scale detection layers (FDL) to autonomously identify concealed cracks of pavement, effectively addressing the issue of missed detection of minor cracks to some extent [10]. ResNet50 vd-deformable convolution (DCN) was used as a backbone of the novel YOLOv3 for pavement crack detection [11]. A method based on pixel points was proposed to quantify and calculate the vertical height of cracks, and combined YOLOv5 with the backbone of ResNet-50 to detect cracks [12]. Next, an automatic detection method based on YOLOv4 with the backbone of CSPDarknet53 was proposed to identify pavement subsurface cracks and voids [13]. Meanwhile, the two-stage target detection framework was also used to detect hidden cracks in pavement, such as Region-CNN (RCNN) [14] and Mask RCNN [15]. ResNeXt-101 with a feature pyramid network (FPN) was selected as the backbone for the Mask RCNN to detect cracks based on GPR B-scan, and the detection performance was improved [16]. Further, an improved Mask RCNN with the backbone of ResNet-101 was proposed to automatically detect and segment small cracks in asphalt pavement at the pixel level [17]. With the use of 3D GPR, the performance of YOLOv3 with the backbone of DarkNet-53, YOLOv4 with the backbone of CSPDarkNet-53, and YOLOv5 the backbone of CSPDarkNet-53 in automatically detecting concealed cracks in asphalt pavement is compared using 3D GPR images [18].