# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 3 Data Description  
### Field Data Collection  
GPR data in this paper were all collected from field detection which was verified by experienced technicians and core drilling. Four urban roads and two highways in China were selected to ensure the diversity of data, and the total length is over 500 km, covering the southern, northern and central cities in China. Table 1 shows the detailed road structure. Four urban roads are in Zhengzhou, Kaifeng, and Xi'an, and two highways are in Anyang and Ningbo. The road structure is semi-rigid pavement that asphalt surface layers on semi-rigid base layers. The surface layer of the urban road consists of the upper layer and lower layer while highways consist of upper layer, middle layer and lower layer. Four-channel GPR system was used for the detection of the whole experiment road because it can be mounted on a vehicle with fast speed and high coverage area. Then, the hand-owed GPR device was used for detailed detection at the location of distresses. The two GPR systems mentioned above are ground-coupled data acquisition that shown in Figure 6. Figure 5(a) shows the Four-channel GPR system with one 200 MHz antenna, two 400 MHz antenna, and one 900 MHz antenna. Figure 5(b) shows the four-channel GPR system mounted on a vehicle. Figure 5(c) shows the hand-towed GPR device with a 400 MHz antenna. The collected GPR image and verified distress are shown in Figure 7. Figure 6(a) shows the GPR image of the cavity. Figure 6(b) shows that a cavity was verified by an industrial endoscope though a 2 cm diameter borehole. Figure 6(c) shows the electric hammer drill working at cavity distress. Figure 6(d) shows the GPR image of the crack. Figure 6(e) shows the crack verified by coring. Figure 6(f) shows the core drilling machine in crack distress. Most of the GPR data for training in this paper was collected by the hand-towing GPR device to ensure the quality of the data. The collected GPR data of cavity and crack distress is shown in Table 2. These samples are insufficient for training the deep learning model, so we could use the data augmentation to raise the number of training samples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Roads**} & \multicolumn{4}{c}{**Urban Road**} & \multicolumn{2}{c}{**Highway**} \\ \hline \multirow{5}{*}{Surfaces} & Location & Zhengzhou & Kaifeng & Xi’an & Huainan & Anyang & Ningbo \\  & Henan & Henan & Shaanvi & Anhui & Henan & Zhejiang \\  & Upper layers & 4 cm AC-16C & 4 cm AC-13C & 4 cm AC-13C & 4 cm AC-13C & 4 cm AC-13C & 4 cm SMA-13 \\  & Middle layers & / & / & / & / & 6 cm AC-16C & 6 cm AC-16C \\  & Lower layers & 6 cm AC-20C & 6 cm AC-16C & 6 cm AC-16C & 6 cm AC-16C & 9 cm AC-20C & 10 cm ATB-25 \\  & Upper bases & 18 cm Cement & 18 cm Cement & 20 cm Cement & 8 cm Cement & 20 cm Cement & 20 cm Cement \\  & & stabilized gavels & stabilized gavels & stabilized gavels & stabilized gavels & stabilized gavels & stabilized gavels \\  & Lower Bases & 18 cm cement & 18 cm cement & 20 cm cement & 20 cm Granular & 18 cm cement & 20 cm cement \\  & & stabilized gavels & stabilized gavels & stabilized gavels & stabilized gavels & stabilized gavels & stabilized gavels \\ \hline \end{tabular}
\end{table}
Table 1: Road structures.  
Figure 6: (**a**) The four-channel GPR system (**b**) GPR system was mounted on a vehicle; (**c**) the hand-towed GPR device.  
Figure 7: (**a**) The GPR image of cavity; (**b**) cavity was verified by an industrial endoscope; (**c**) field picture at cavity distress location; (**d**) the GPR image of crack; (**e**) crack was verified by coring; (**f**) field picture at crack distress location.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Set** & **Validation Set** & **Testing Set** \\ \hline The number of cavities & 312 & 48 & 48 \\ The number of cracks & 271 & 63 & 63 \\ The number of images & 547 & 108 & 108 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of collected field data.