# Pothole Classification Model Using Edge Detection in Road Image
## 4 Result and Performance Evaluation
### Performance Evaluation  
In this paper, as an environment for performance evaluation, Windows 10, Intel Core i5-8400, 16 GB RAM, and 2070 super GPU are used. The Global Road Damage Detection Challenge 2020 data set is used for performance evaluation [48]. This includes images of damaged roads such as potholes and cracks on roads in Japan, India and the Czech Republic. Road damage information is provided through a label indicating the type of damage associated with the coordinates of the bounding box. There are 13,376 road damage image data in the data set. Among them, in this paper, to determine the existence or non-existence of a pothole, damage information other than the pothole is classified as non-pothole. The data used to evaluate the pothole classification model using edge detection in road image is converted to a gray-scale to reduce the amount of computation. Also, other objects except for potholes are detected using the YOLO algorithm to perform preprocessing in the background. It's divided into 20% validation data set, 10% test data set, and 70% training data set to prevent overfitting. Also, the data collected directly is used as a verification data set. Accordingly, the feature is extracted by detecting the edge of the pothole, and the pothole is detected and classified based on YOLO. In the performance evaluation, the distortion rate and restoration rate of the image data are evaluated, the result of pothole classification using the model and the effectiveness evaluation, and the performance comparison with the existing model is conducted. The first evaluation method uses MSE, PSNR, and SSIM to evaluate the distortion rate and restoration rate. The classification results and effectiveness using the proposed model are evaluated through AUC (area under curve) using ROC based on the accuracy and confusion matrix. In addition, the performance comparison with the existing model is compared for the classification accuracy. Data used for performance evaluation is pre-processed, and distortion occurs in the original. Therefore, it is evaluated whether effective pre-processing has been performed by evaluating the image distortion rate and restoration rate. Therefore, the first performance evaluation evaluates the distortion rate and restoration rate of the data. Mean Square Error (\(MSE\)) for this means the mean square error and compares the measured value for the difference between pixel values. This is a method of measuring the difference in pixel values between the original image and the output image. Equation (3) represents \(MSE\). \[MSE=\frac{1}{AB}\sum_{i=0}^{A-1}\sum_{j=0}^{B-1}\left[I(i,j)-K(i,j)\right]^{2} \tag{3}\]  
In Equation (3), \(I\) refer to a gray-scale image of \(A\times B\) size. \(K\) refers to an image with noise in \(I\). Image and video data are subject to data loss during compression and differ from original data. \(PSNR\) is used to measure this difference. The \(PSNR\) stands for the Peak Signal-to-Noise Ratio, which means the ratio of noise to maximum signal that a signal can have. This is an index used to determine image quality loss information in video or loss compression. Also, the smaller the loss, the higher the value. For lossless images, the \(PSNR\) is not defined because the mean squared error is 0. Equation (4) represents \(PSNR\). \[PSNR=10\log\frac{Max_{I}^{2}}{MSE} \tag{4}\]  
In Equation (4), \(MAX_{I}\) represents the maximum value of the corresponding video or image, and in the case of a gray-scale video or image, the maximum value is 255. This means that the smaller the \(MSE\), the higher the \(PSNR\). Accordingly, the \(PSNR\) value is higher in the data with good quality than in the data with poor quality. In addition, the SSIM is used to evaluate visual similarity through differences in visual quality, not numerical errors. The Structural similarity Index Map (SSIM) means a structural similarity index. This is a method of measuring similarity to original data for distortion caused by compression and transformation.