# Pothole Classification Model Using Edge Detection in Road Image
## 4 Result and Performance Evaluation
### Performance Evaluation
On the other hand, Pothole_9 predicts that the pothole is not present in the image but is a pothole at 75%. This is because a manhole was included in the road and it was recognized as a pothole. In addition, the pothole and other road damage results are included at the same time, such as the existence of multiple potholes on the road in the same area, or both potholes and cracks. In order to evaluate the accuracy of the pothole detection using the proposed method, evaluation is performed on sample data resulting from damage to the pothole and other roads. Evaluation evaluates the accuracy of the classification. This uses 6 sample data from the complex pothole. Complex_1 is an image that includes lines, potholes, and cracks. Complex_2 is an image of multiple potholes. Complex_3 is an image that includes cars, motorcycles, and potholes. Complex_4 is an image containing lines and potholes. In addition, Complex_5 is an image that includes pothole and line at night time. Complex_6 is an image containing cracks, lines, and cars. Table 4 shows the classification results for potholes and other road damage. This is represented by the composition, accuracy, and classification results of sample data. Data organization is divided into cracks, potholes, lines, and other objects. In the classification result of Table 4, the average accuracy of the pothole classification result is about 74%. All 6 sample data were extracted with an accuracy of 65% or more. Complex_1, Complex_2, and Complex_3 have 2 or more potholes. However, it correctly classifies the pothole. On the other hand, Complex_4 and Complex_5 have potholes, but the characteristics of the line are more prominent and extracted. In the case of Complex_4, a pothole and a line can be visually distinguished, but the model detects a line with a clearer outline than a pothole. Therefore, it is classified as non-pothole. Also, Complex_5 is an image of a rainy night. Therefore, when converting to gray-scale, it is difficult to \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{4}{c}{**Configuration**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Classification Result**} \\ \cline{2-2} \cline{4-7}  & **Crack** & **Pothole** & & & **Line** & **Other** \\ \hline Complex\_1 & 2 & 4 & 2 & 0 & 77.6\% & Pothole \\ Complex\_2 & 0 & 3 & 0 & 0 & 68.4\% & Pothole \\ Complex\_3 & 0 & 2 & 0 & 2 & 75.6\% & Pothole \\ Complex\_4 & 0 & 1 & 1 & 0 & 65.6\% & Non-Pothole \\ Complex\_5 & 0 & 1 & 2 & 0 & 83.3\% & Non-Pothole \\ Complex\_6 & 2 & 0 & 1 & 1 & 74.2\% & Non-Pothole \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The classification results for potholes and other road damage. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**NO** & **Configuration** & **Accuracy** & **Classification Result** \\ \hline Pothole\_1 & Pothole 50\%, etc., 50\% & 96.2\% & Pothole \\ Pothole\_2 & Pothole 90\%, Crack 10\% & 75.5\% & Pothole \\ Pothole\_3 & Pothole 100\% & 82.3\% & Pothole \\ Pothole\_4 & Pothole 30\%, Crack 60\%, etc., 10\% & 60.5\% & Non-Pothole \\ Pothole\_5 & Pothole 30\%, Crack 30\%, etc., 40\% & 58.9\% & Non-Pothole \\ Pothole\_6 & Pothole 70\%, etc., 30\% & 87.5\% & Pothole \\ Pothole\_7 & Pothole 90\%, etc., 10\% & 93.2\% & Pothole \\ Pothole\_8 & Crack 60\%, etc., 40\% & 68.1\% & Non-Pothole \\ Pothole\_9 & etc., 100\% & 75.6\% & Pothole \\ Pothole\_10 & etc., 100\% & 80.8\% & Non-Pothole \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of pothole classification accuracy using the proposed model. find the outline of the pothole because of the difference, in contrast, is small. Accordingly, it is classified as having a pothole but non-pothole. Also, Complex_6 is classified as not an existence pothole because there are non-pothole and cracks, lines, and other objects that exist. To evaluate the validity of the model using the confusion matrix. Table 5 shows the confusion matrix for evaluating the pothole classification model. True indicates a pothole, while false indicates a non-pothole.