# Pothole Classification Model Using Edge Detection in Road Image
## 5 Discussion  
Potholes and cracks in road damage are characterized by various shapes and sizes. This causes road traffic accidents when the size is large. Therefore, a pothole, which is potential danger is detected to ensure the safety of drivers, fellow passengers, and cars driving on the road. In this study, a pothole classification model using edge detection in road image is proposed. The proposed method is a method for detecting potholes in road damage image data. This was converted to a gray-scale to reduce the amount of computation of the RGB pothole image. In addition, in order to increase the accuracy of the detection of potholes, other objects except the pothole were detected using the YOLO algorithm and removed. Accordingly, a value of 255 was applied to the background to perform the preprocessing process to convert it to a white background. In addition, the pothole is lighter or darker than the surrounding roads. Therefore, the part where the brightness of the edge changes is detected. Accordingly, the partial differential operator for edge detection was used to extract the edge according to the gradient change. Also, the Laplacian mask was used as a model filter to extract the edges along with all directions of the pothole. By extracting the edge, the characteristics of the edge of the pothole were identified. In addition, based on YOLO, classification is performed according to the presence or absence of a pothole. In the performance evaluation of the proposed method, the distortion rate and restoration rate of the image data were evaluated, the result of pothole classification using the model and the effectiveness evaluation, and the performance comparison with the existing model was conducted. First, the distortion rate and restoration rate of image data were evaluated using MSE, PSNR, and SSIM. As a result of the evaluation, the MSE has an error of 0.2 to 0.44. PSNR was evaluated to be 50 db or more. In addition, SSIM was evaluated at 0.71 to 0.82. In the second performance evaluation, the accuracy, precision and validity of the model were evaluated by classifying potholes through the proposed method. As a result of the evaluation, when there is one pothole in the image data, the average accuracy was about 77.86% and the average accuracy was about 83%. On the other hand, if there are multiple potholes or the results of the pothole and other road damage are included at the same time, the average accuracy is about 74% and the average accuracy is about 75%. In addition, the validity of the model for a single pothole was evaluated as 0.9 using AUC. On the other hand, in the pothole classification in a complex environment, an AUC of 0.733 was evaluated. Therefore, we proved that the proposed model is a useful model for classifying potholes in a single pothole and a complex environment. In the third performance evaluation, the performance comparison of precision and accuracy according to the existing model and classification was conducted. As a result of the evaluation, the proposed method was excellently evaluated in terms of precision and accuracy. Accordingly, the characteristics of the shape and size of potholes can be identified through the proposed method. In addition, image data can be analyzed effectively by reducing the loss rate on image data. However, the image data analysis using the proposed method can detect the shape of a pothole, but there are difficulties in predicting the actual size of a pothole. In addition, the performance of classification is evaluated low in an image in which there are many cracks around a shadow or pothole. Therefore, there is a need for a method that can maintain and manage roads continuously by identifying detailed characteristics and predicting the size of potholes. In the future, we plan to study how to extract detailed features of the pit in order to predict the actual size of the pit from the image data. **Author Contributions:** [PERSON]-[PERSON] and [PERSON] conceived and designed the framework. [PERSON]-[PERSON] performed experiments and analyzed the results.