# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 1 Introduction
At present, the CNN model can be categorized into a one-stage detector and a two-stage detector. The two stages of a two-stage detector can be divided into generating proposals and recognizing the proposals. The typical two-stage detectors in GPR images include Faster R-CNN [14], Mask R-CNN [15], and Cascade R-CNN [16]. Although the two-stage detector can achieve high detection accuracy, they lost the detection speed greatly. The one-stage detector can accelerate the speed of detection using a single end-to-end model [17], including single shot multibox detector (SSD) [18], you only look once (YOLO) [19], CenterNet [20] and RetinaNet [21]. Among these, the YOLO model is the most widely used in GPR image detection. With the development of CNN, the YOLO version has been updated from YOLOV3 to YOLOV7; YOLO V7 is the latest version of the YOLO which ensures efficient model inference and high accuracy [22]. A significant number of weight parameters need to be specified when training the CNN model, which requires lots of labeled GPR images. However, obtaining the ground-truth subsurface distress labels is challenging as they are invisible. The insufficient number of training sets will cause instability of the training network, difficulty in reducing the loss function, and overfitting in the deep network [23]. Currently, three leading solutions are used to address these problems: transfer learning, architecture network modification, and data augmentation. Transfer learning is a method that uses a pre-trained model from the natural datasets, and then this model is reused as the basis for learning a model for a new task [24]. However, the natural datasets used for pre-training are quite different from the GPR radargram. This method cannot effectively improve recognition accuracy and training efficiency. Most CNN architectures are designed for general image detection tasks, and these models may not be suitable for GPR image analysis. So, architecture network modification is another optimizing method, such as choosing a strong classification layer, modifying anchor scale, and size, etc. [25]. However, this method is challenging to maintain high accuracy and low data requirement simultaneously. Data augmentation is another widely used method for model optimization, which is one of the most effective methods to overcome insufficient GPR training datasets [26]. Data augmentation techniques belong to the category of data warping, which is a strategy for increasing the variability and volume of training data in data space [27]. At present, there are three main data augmentation methods: (a) Performing color and geometric augmentations are the simple data augmentation strategy to establish the GPR datasets [28], such as rotating, flipping, and cropping input images, changing the color space of the images, and reflecting the images, etc. [PERSON] et al. used the strategy of horizontal flipping and image scaling 1.5 and 2 times for data augmentation when detecting rebar in concrete using GPR. [PERSON] et al. applied the same strategy for data augmentation, including flipping, stretching, compression, and image enhancement, which makes the deep learning model less susceptible to details in limited datasets [8]. [PERSON] et al. used a computer vision tool, Albumentations [29], to augment the collected GPR data, involving a combination of blurring, cropping, rotation, mirror flip, etc. [30]. However, these data augmentation strategies may not be suitable in consideration of the characteristics of GPR data because the reflection features of the target also have fixed directions. Additionally, the real feature of the target would be changed by using rotation and other strategies, which can easily cause the model's poor generalization ability. (b) The second method is based on the finite-difference time-domain (FDTD) simulation to generate more GPR images [31]. However, these simulated images have discrepancies with the real field data because of lacking the background clutter caused by subsurface environmental noise.