# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 2 Methods
### Deep Learning Model  
YOLO V7 is modified and trained for subsurface distress detection in this paper [22]. YOLO V7 is the latest version of the YOLO (You Only Look Once) series. As a one-stage object detection model, it takes the image as input and directly regresses the object class and coordinates via backpropagation. The processed reflected radar signal was mapped into a three-channel image and used as the input. When subsurface distress exists in the image, the model outputs an array of \((x,y,w,h,c)\), represents the horizontal and vertical coordinates, length, width and distress type, respectively. The model consists of three parts: input, backbone, and head. The input layer first pre-processes the input image and aligns to a 640 \(\times\) 640 or 416 \(\times\) 416 size RGB image. The processed images are fed into the backbone network to extract features. Based on the output of the three layers in the backbone network, the head layer fuses them and performs classification and bounding box coordinates prediction as the final result.  
Ensuring efficient model inference while maintaining high accuracy has been a thorny problem. The model mainly addresses this challenge through the network architecture design and adjustment of the training strategy. In terms of network architecture design, YOLO V7 extends the efficient long-range attention network (ELAN) [38], called Extended-ELAN (E-ELAN). In large-scale ELAN, the network can reach a stable state regardless of the gradient path length and the number of blocks. E-ELAN expands, shuffles, and merges cardinality to improve the network's learning ability without destroying the original gradient paths. The network's learning ability can be improved without destroying the original gradient path. In addition to maintaining the original ELAN design architecture, E-ELAN can also guide different groups of computational blocks to learn more diverse features.  
In addition, YOLOv7 uses a concatenation-based model scaling approach. The primary purpose of model scaling is to generate models of different scales to meet the needs of different inference speeds. Traditional scaling methods perform the scaling-up or scaling-down process without changing the in-degree and out-degree of each model layer. However, suppose these methods are applied to a concatenation-based architecture. In that case,  
Figure 5: (**a**) The original GPR image at gray mapping rules; (**b–h**) are the GPR image at afmhot, bwr, cool, jet, ocean, rainbow, and RdGy mapping rules, respectively.  
it is found that the computational block of the concatenate-based transformation layer decreases or increases when the execution depth is scaled up or down. Therefore, this model proposes that when scaling the depth factor of a computation block, the change in the output channel of the block must also be calculated. Then, the transition layer will be scaled with an equal change in width factor. This composite scaling method maintains the model's characteristics at the initial design time and maintains the optimal structure.  
In terms of training strategy, the authors introduce soft labels, which consider the distribution of prediction results and ground truth, into the model training. Unlike the usual soft label training, the model adds a \"label allocator\". This allocator uses the predictions of the lead head as a guide to generate coarse to fine hierarchical labels for the learning of the auxiliary head and the lead head, respectively. This allocation gives the lead head a stronger learning capability. In addition, the generated soft labels are more representative of the differences and correlations between the source data and the object.