# Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning
## 1 Introduction  
Three-dimensional ground penetrating radar (3D GPR) is an emerging non-destructive inspection technology that is efficient, accurate, and multi-dimensional [1, 2]. It has been a major tool for pavement distress detection and pavement condition evaluation [3, 4, 5]. Pavement distress detection is defined as the process of classifying and locating instances of pavement distresses in images or videos. Compared to a 2D GPR, 3D GPR uses stepping frequency and antenna array technologies to collect the full structure data of a pavement section. Informative 3D data can be used to detect internal pavement distress, such as cracks, repairs, voids, poor interlayer bonding, and mixture segregation [6]. However, a 3D GPR can obtain thousands of radar images per kilometer in different dimensions. GPR images have been processed using traditional machine learning algorithms in several studies [7]. [PERSON] et al. [8] combined support vector machines (SVMs) and hidden Markov models (HMMs) for Crevasse detection in ice sheets. [PERSON] et al. [9] combined SVM with H-Alpha Decomposition for subsurface target classification of GPR. The existing processing methods are fallible and unreliable for pavement distress detection using 3D GPR data. In the last decade, machine learning has produced breakthroughs due to the rapid development of deep learning. Deep learning provided excellent performance in the fields of image recognition, speech recognition, and information security [10; 11; 12]. In particular, convolutional neural networks (CNNs) have made excellent achievements in object detection thanks to their powerful feature extraction architecture [13; 14]. Additionally, the vision transformer (ViT) model, which has emerged in the last two years, also has achieved remarkable success in object detection [15; 16]. Such cases provide a new idea for GPR image processing [17]. For example, [PERSON] et al. [18] detected and located reinforced steel bars in concrete using GPR images and a Single Shot MultiBox Detector (SSD) model with good accuracy and detection speed. [PERSON] et al. [19] and [PERSON] et al. [20] achieved the automatic detection of concealed cracks and voids by using the You Only Look Once (YOLO) model and 3D GPR, respectively. [PERSON] et al. [21] proposed three CNN models to classify, localize, and measure structural cracks and potholes in asphalt pavements using GPR images. [PERSON] et al. [22] proposed a data enhancement method based on a convolutional self-encoder structure, which significantly improved the accuracy of crack classification. [PERSON] et al. [23] proposed a pavement distress detection model based on faster region convolutional neural network (Faster R-ConvNet), which reduced the ratio of missing and false detection. In addition, [PERSON] et al. [24] used cascaded CNN models to overcome the low-accuracy problem of traditional CNNs in identifying low-resolution GPR images. [PERSON] et al. and [PERSON] et al. [25; 26; 27] used GPR images to identify, locate, measure, and reassemble 3D models of internal cracks by building CNN models. In addition, they developed a Faster R-ConvNet model to accurately identify internal pavement distress (reflection cracks, water-damage pits, and uneven settlements). Moreover, GPR signals were directly input into a network-in-network architecture to achieve the training and testing of the model, and the results showed that the method was effective in detecting cracks, water-damage pits, and uneven settlements. [PERSON] [28] used a 3D-to-2D data transformer for reverse-time offset imaging and adopted a single-shot detector to investigate subsurface structures. [PERSON] et al. [29] implemented GPR data enhancement using cycle generative adversarial networks and localized radar hyperbolic waveforms by a Faster R-ConvNet. [PERSON] et al. [30] combined GPR images from different channels into ones for pavement distress detection, and the results showed that the method effectively reduced the error rate. [PERSON] et al.