# 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  
Road damage is caused by various causes, such as weather, traffic volume, vehicles, and road pavement conditions. There are various types of road damage, such as cracks, potholes, sinkholes, erased lanes and crosswalks, and decomposition. In particular, there is a difference in the degree of danger depending on the depth or size of the pothole. You may pass by small potholes indifferently. On the other hand, large potholes damage the tires of driving vehicles, increasing the risk of accidents. Therefore, it is necessary to extract and deal with the characteristics of potholes. To this end, detection is conducted along the edge of the pothole and its size is estimated. The edge means the boundary between two areas with different brightness. The types of edges include loop edge [37], line edge [38], step edge [39], and ramp edge [40]. The loop edge indicates a place where the brightness of the image gradually changes and then returns again at a certain point [37]. The line edge indicates a place where the brightness of the image changes suddenly at a point, but returns over time [38]. The step edge indicates a place where the brightness of the image changes suddenly [39]. The lamp edge indicates a place where the projection brightness changes gradually [40]. The edge detection for extracting the features of the edge is a method of extracting the pixel corresponding to the edge to obtain the gradient through the partial differential operation [41]. This identifies the existence of the edge in the image through the magnitude of the graph gradient using the first differential. The second differential is used to identify the position of the light and dark parts of the edge pixels through the sign of the graph gradient. In the road damage image data, the pothole is darker than the surroundings. Therefore, it is possible to extract features in real-time through edge detection. To extract the edge, we use a mask that acts as a differential operator. The mask means a filter that meets mathematical conditions and has the same effect as a differential operator. The feature is that the sum of all pixels in the mask is 0. This is a filter used in noise reduction, sharpening, edge  
Figure 2: Pre-processing process using object detection. detection, etc. in image processing. The masks include Sobel mask [42], Prewitt mask [43], Robert mask [44], Laplacian mask [45] and Canny mask [46]. The Sobel mask is very sensitive to brightness because it detects even noise as an edge [42]. The Prewitt mask has similar results to those of the Sobel mask. On the other hand, since the calculation speed is faster than Sobel and there is a difference in the specific gravity of the brightness boundary, the edge is relatively weak [43]. The Robert mask is very sensitive to noise and only detects edges with distinct characteristics [44]. The Laplacian mask detects edges with sharp features. In addition, the calculation speed is fast, and edges in all directions can be detected [45]. Being resistant to noise, the Canny mask has the advantage of utilizing various edge detection masks. However, several masks should be used because only clear edges are detected [46]. To select a filter for extracting the correct pothole contour among Sobel, Prewitt, Robert, Laplacian, and Canny masks through evaluation using PSNR. Accordingly, the mask with the best average value is used as the filter of the model. Table 1 shows the PSNR results according to the mask type. In Table 1, the average was extracted by evaluating PSNR for each mask through the data of four samples. The PSNR evaluation resulted in Sobel of 49.672, Prewitt of 50.156, [PERSON] of 46.852, Laplacian of 54.859, and Canny of 52.020. With Laplacian, the highest PSNR values are extracted. Therefore, Laplacian is used as a filter to extract pothole geometry. In the extracted edge, if the position, shape, or size of the object changes, accuracy is reduced in finding the characteristics of the object.