# Pothole Classification Model Using Edge Detection in Road Image
## 4 Result and Performance Evaluation
### Performance Evaluation
_TP_ means a case where the prediction result is judged to be a pothole when it is an actual pothole, _FN_ means a case where the prediction result is judged not to be a pothole when it is an actual pothole, _FP_ means a case where the prediction result is judged to be a pothole when it is not an actual pothole and _TN_ means a case where the prediction result is judged not to be a pothole when it is not an actual pothole. Table 6 shows the classification results using the confusion matrix. This is a classification result for the case of one pothole. Table 6 shows 5 results for TP, 2 for FN, 1 for FP, and 2 for TN. This is about 83% precision. To evaluate the classification results and the effectiveness of the model, it is evaluated through AUC using ROC. The ROC evaluates the performance of the model at thresholds for all classifications [52], AUC indicates the area under the ROC and evaluates performance through a value of 0-1. This means that the closer to 1, the better the performance of the classification model [53]. Figure 8 shows the results of pothole classification using AUC. The horizontal axis represents FPR (False Positive Rate). The vertical axis represents TPR (True Positive Rate). In the pothole classification result using the AUC in Figure 8, the area of the AUC is evaluated to be 0.9. This means that the pothole images were accurately classified according to the presence or absence of the pothole. Therefore, the proposed model is suitable for classifying potholes In addition, Table 7 shows the results of pothole classification in a complex environment. In Table 7, there are 3 results for TP, 2 for FN, 1 for FP, and 0 for TN. This is about 75% precision. Figure 9 is the evaluation result of the model for the pothole classification result in a complex environment using AUC. As a result of evaluating the pothole classification model in a complex environment using the AUC in Figure 9, the area of AUC is estimated to be about 0.733. This model is suitable for classifying potholes even in a complex environment. Finally, the third performance evaluation compares the classification accuracy and precision of the proposed method and the existing pothole detection method. This is evaluated using a confusion matrix. Table 8 shows the average of the classification accuracy and precision comparison results of the proposed method and the existing pothole detection method. As a result of Table 8, the proposed method is evaluated better in terms of precision and accuracy of pothole detection and classification than the conventional method. [PERSON] et al. (2018) in the case \begin{table}
\begin{tabular}{c c c} \hline \hline
**ActualPredicted** & **True** & **False** \\ \hline True & 3 & 2 \\ False & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of pothole classification in a complex environment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Precision** & **Accuracy** \\ \hline Ours & 0.8345 & 0.7786 \\ [PERSON] et al. (2018) & 0.8115 & 0.7284 \\ [PERSON] et al. (2018) & 0.7159 & 0.6438 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The average of the classification accuracy and precision comparison results of the proposed method and the existing pothole detection method. Figure 9: Evaluation results of pothole classification model in a complex environment using AUC. of the method, image data with only pothole is used. Therefore, the performance is evaluated low because the pothole cannot be properly detected in the image where other objects are present. Also, [PERSON] et al. [55] in the case of the method, it is limited to the road image of asphalt. It is also possible if the shape of the pothole is similar. Therefore, the performance is evaluated poorly in image data of different shapes. Therefore, the proposed method shows excellent performance even in various images because it considers whether several objects are included.