# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 5 Conclusions  
In this paper, we have proposed GPR data augmentation methods based on incorporating domain knowledge. The YOLOv7 model trains the augmented datasets to realize the automatic detection of the subsurface cavity and crack. These data augmentation methods can decrease loss function values when the training epochs grow. Additionally, the performance of the deep learning model gradually became stable when the original datasets were augmented two times, four times, and eight times, proving that the augmented datasets can increase the robustness of the training model. Compared with the traditional data augmentation method, the data augmentation method proposed in this paper incorporates domain knowledge of GPR. The gain compensation strategy considers the effect of gain on radar imaging, which can augment the datasets by simulating the EW propagation through different mediums. The station spacing strategy takes into account the impact of the setting parameters of the GPR device, which can augment the datasets by setting different station spacing when collecting the data. The radar signal imaging strategy takes into account the effect of the pixel value range when mapping the amplitude values into the image. In addition, our proposed method achieved higher precision, recall, F1_score, and mAP_0.5 compared with the traditional data augmentation method. However, we only chose a single data augmentation method and one deep learning model (YOLOv7) to study the impact of the model in this paper. Thus, a combination of different techniques and different deep learning models (such as Faster_CNN, SSD, Transformer, etc.), will be considered and compared for future work.  
Conceptualization, G.Y. and C.L.; methodology, G.Y.; C.L. and S.G.; software, Y.L.; validation, Y.L. and S.G.; formal analysis, G.Y.; investigation, G.Y.; data curation, G.Y. and S.G.; writing--original draft preparation, G.Y.; writing--review and editing, G.Y.; project administration, C.L.; funding acquisition, Y.D. and C.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Augmentations**} & \multicolumn{3}{c}{**Original**} & \multicolumn{3}{c}{**Gain Compensation**} & \multicolumn{3}{c}{**Station Spacing**} & \multicolumn{3}{c}{
\begin{tabular}{c} **Radar Signal** \\ **Mapping Rules** \\ \end{tabular} } & \multicolumn{3}{c}{**Traditional Method**} \\ \cline{2-13}  & **Cavity** & **Crack** & **All** & **Cavity** & **Crack** & **All** & **Cavity** & **Crack** & **All** & **Cavity** & **Crack** & **All** & **Cavity** & **Crack** & **All** \\ \hline precision & 0.584 & 0.912 & 0.748 & 0.747 & 0.922 & 0.835 & 0.769 & 0.879 & 0.824 & 0.763 & 0.899 & 0.831 & 0.663 & 0.849 & 0.756 \\ recall & 0.791 & 0.619 & 0.705 & 0.854 & 0.713 & 0.784 & 0.833 & 0.623 & 0.728 & 0.821 & 0.652 & 0.737 & 0.778 & 0.649 & 0.714 \\ F1\_score & 0.672 & 0.737 & 0.726 & 0.797 & 0.804 & 0.809 & 0.800 & 0.729 & 0.773 & 0.791 & 0.756 & 0.781 & 0.716 & 0.736 & 0.734 \\ mAP\_0.5 & 0.731 & 0.820 & 0.776 & 0.796 & 0.827 & 0.811 & 0.811 & 0.781 & 0.796 & 0.797 & 0.819 & 0.808 & 0.815 & 0.749 & 0.782 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of model comparison using different augmented data for training.  
This work is supported by the National key research and development program of China (2021 YFB1600100), and the Scientific Research Project of the Shanghai Science and Technology Commission (21 DZ1200601).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.