# Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning
## 2 Proposed Approaches
### Proposed Classification Model  
#### 2.3.1 Distress Classification Dataset  
Even though the proposed location model can determine anomalous waveform areas, it cannot easily determine the categories of these areas. For example, there was no easy method to determine if the anomalous waveform area was caused by cracks or uneven settlement. The main reason for this was that few studies had demonstrated the ability of horizontal GPR images to represent different types of pavement distress. However, the class-membership strategies of anomalous waveform images affected distress classification. An over-fine classification strategy had the risk of misclassification, despite sometimes providing a precise decision; an over-coarse strategy cannot provide an informative decision. In this study, three class-membership strategies, as shown in Table 4, were proposed to demonstrate the capacity of horizontal GPR images to represent different pavement distresses. Among them, HD1 (Horizontal Distress 1) indicated that all anomalous waveforms were considered cracks; HD16 indicated that the anomalous waveforms could represent cracks or noises; HD45 indicated that the anomalous waveforms may be poor interlayer bonding or mixture segregation; HD6 indicated that the anomalous waveforms were background noises.  
As shown in Table 1, 9045 anomalous waveforms were manually labeled, which were then intercepted from the horizontal radar images to create a classification dataset. Figure 11 illustrates how the image interception method works. The anomalous waveform areas were cropped from the radar images based on the position coordinates. The intercepted anomalous waveform images had different shapes, while a classification model was required to had the inputs with a fixed size. Thus, the cropped anomalous waveform images were resized by adding zeros to the missing parts. Finally, these filled images were assigned to one of the distress categories using different class-membership strategies. Therefore, three datasets with the same images but different labels were generated, as shown in Table 5.  
In all three datasets, the number of samples in different categories was balanced. In the 5-category dataset, all 395 images were selected for HD126, and 400 images were randomly  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & & & **Category** & & **Category** \\
**Method** & & & & & **Number** \\ \hline
1 & HD1 & HD16 & HD45 & HD6 & HD126 & 5 \\
2 & HD1 & HD16 & HD45 & HD6 & 4 \\
3 & HD1 & HD45 & HD6 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Class-membership strategies of anomalous waveform in horizontal radar image.  
Figure 11: Process of intercepting anomalous waveform areas from a horizontal radar image. The red boxes are manual labeled.  
selected for each of the other categories to create the dataset, and the total number of datasets was 1995. In the 4-category dataset, the number of randomly selected images were 1072, 1100, 1100, and 1100, respectively, for a total of 4372 images. In the 3-class dataset, the numbers of randomly selected images were 1600, 1366, and 1600, and the total number of images was 4566. For the three classification methods, the ratio of the training and validation sets was 8:2, and the test set consisted of all the anomalous waveforms in 600 horizontal radar images with a total of 1592 images.