# Pothole Classification Model Using Edge Detection in Road Image
## 3 Pothole Classification Model Using Edge Detection in Road Image
### Feature Extraction of Road Damage Using Edge Detection
Therefore, each feature point is extracted from the extracted edge. The feature point can be easily detected even if the shape, size, or position of the object changes. In addition, the object can be easily detected even when the camera position and lighting change. Figure 3 shows the process of extracting features of road damage using edge detection. In Figure 3, feature extraction of road damage using edge detection proceeds in two stages. In the first stage, the partial differential operator is used. Since the edge is a part where the brightness is changed, the differential is used to extract the edge according to the gradient change. In the second stage, the second differential is performed using a mask. The mask uses Laplacian to extract the edge along with all directions of the pothole. Accordingly, the feature of the edge is identified by extracting the edge. The size of the filter used at this time is evaluated as \(1\times 1\), \(2\times 2\), \(3\times 3\), \(4\times 4\), \(5\times 5\), and the size of the filter with the highest accuracy is used. In general, the size of the filter is odd. When using an odd number of filter sizes, there is a center pixel. Accordingly, the cell value of the filter is calculated evenly, and it is possible to output as one value representing the filter. On the other hand, when the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**DATA\_PSNR of Mask**} & \multicolumn{4}{c}{**PSNR (Peak Signal to Noise Ratio)**} \\ \cline{2-6}  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline DATA\_1 & 52.345 & 51.246 & 50.235 & 57.215 & 56.112 \\ DATA\_2 & 48.231 & 49.215 & 45.248 & 54.857 & 52.210 \\ DATA\_3 & 47.564 & 48.842 & 45.987 & 52.241 & 48.542 \\ DATA\_4 & 50.542 & 51.321 & 44.857 & 55.123 & 51.215 \\ Average & 49.672 & 50.156 & 46.582 & 54.859 & 52.020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The peak signal to noise ratio (PSNR) results according to the mask type. Figure 3: The process of extracting features of road damage using edge detection. filter size is used as an even number, asymmetric results are extracted. Therefore, the computational cost for feature extraction is very high, and feature learning is difficult. However, in this paper, the sizes of \(1\times 1,2\times 2,3\times 3,4\times 4\), and \(5\times 5\) filters are compared in order to find out the change in accuracy when the filter size is gradually increased. The \(1\times 1\) filter uses the original image. Figure 4 shows the accuracy according to the size of the filter using the Laplacian mask. The vertical axis represents accuracy, and the horizontal axis represents the size of the filter. As a result of the accuracy evaluation in Figure 4, if the size is \(1\times 1\) and \(3\times 3\) among the five filters, the level is high. At this time, the filter of \(1\times 1\) size represents the result when the original image is used. At this time, it shows an accuracy of about 75% even without applying a filter. However, in the case of the size of \(4\times 4\) and \(5\times 5\), the accuracy decreases. In the case of \(5\times 5\), it is judged that detailed features could not be found because the size of the filter is large. In addition, since \(2\times 2\) and \(4\times 4\) are even-numbered filters, it is judged that an asymmetric problem, an error in learning, and an error in feature extraction occurred. On the other hand, when the size of the filter is \(3\times 3\), it is a peak, and the accuracy is high. Therefore, when the filter size is \(3\times 3\), the pothole feature is most effectively extracted, so the model filter uses the \(3\times 3\) size.