# Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI
## 1 Introduction  
Ground-penetrating radar (GPR) is a non-destructive detection method for the subsurface environment widely used in the fields of tunnel health conditions [1], underground pipelines [2], and highway structural distress detection [3]. The use of GPR in highway structural distress detection generates a huge amount of data. Even experienced practitioners spend significant time and effort interpreting GPR data. Manual interpretation is costly, inefficient, and lacks standardized reading criteria, making accuracy hard to guarantee, and, thus, greatly limiting its application in the field [4]. Deep learning [5] is a subclass of machine learning (ML) that uses techniques such as convolutional neural networks (CNNs) to learn features from images. CNNs, with multiple hidden layers, leverage learning from previous layers during model training [6], exhibiting a strong capability in recognizing computer vision features and high robustness in detection tasks. In recent years, deep learning technology has been developed vigorously, and many scholars have tried to apply deep learning to the interpretation of GPR data. [PERSON] et al. [7] used the deep learning model YOLO to detect hidden cracks in GPR images to realize the automatic identification and localization of hidden cracks in asphalt pavement. Three versions, namely YOLOv3, YOLOv4, and YOLOv5, were trained and compared, and finally, YOLOv5 was obtained. The three versions of YOLOv3, YOLOv4, and YOLOv5 were trained and compared, and finally, the conclusion that the YOLOv4 model is the most balanced deep learning model in terms of speed and actual performance in detecting hidden cracks was obtained. [PERSON] et al. [8] improved the problem of false and misseddetection in real-time GPR detection by improving the network structure of YOLOv5, adding the attention mechanism and data augmentation, and then the regression equation for the location information of the ground-penetrating radar's position information to establish a regression equation, which realized the accurate location of foreign objects in underground soil. [PERSON] et al. [9] denoised the original GPR road detection image and then tested the accuracy of the original and denoised images using YOLOv3, and the final detection accuracy was improved by 30%. [PERSON] et al. [10] established a dataset of asphalt pavement distresses including settlement, hidden cracks, and loosening, and then proposed a two-way object detection model for targets of different scales based on the distribution of hidden distresses in the GPR image data. The components of the two-way model were optimized and improved based on the basic model, and the final model's average precision (AP) for small targets was improved by 17.9%, and the combined index AP (0.5:0.95) was improved by 9.9%. [PERSON] et al. [11] proposed a Faster R-ConvNets object detection model, enabling intelligent detection of water damage, cracks, and uneven settlement damage in GPR images, with model precision and recall reaching 0.89. However, training a reliable deep learning model requires a large amount of GPR data labeled with underground targets, which are often difficult to obtain due to the high cost of data collection and field validation. With less data, the scarcity of samples often leads to low recognition accuracy when performing model training. Therefore, data augmentation of GPR data remains an urgent problem. Generative adversarial networks (GANs) [12] are widely used to enrich the number and diversity of datasets, providing a new avenue for GPR data enhancement. [PERSON] et al. [13] proposed an improved least squares generative adversarial network (LSGAN) model, which can generate high-precision GPR data and solve the scarcity of labeled GPR data. [PERSON] et al. [14] proposed a Wasserstein GAN (WAEGAN) based on generative adversarial networks (GANs) and verified that the method is effective in simultaneously generating multiple target classes and generating realistic GPR data.