# Pothole Classification Model Using Edge Detection in Road Image
## 1 Introduction  
Computer vision is a technology that extracts useful information by inputting visual data into a computer and analyzing it [1, 2]. This is a technology being used in various fields such as robots, medical treatment, security, games, and road transportation by using images and images through deep learning as information technology develops. The goal of computer vision is to extract interesting information from pattern recognition, statistical learning, and projection geometry through object detection, segmentation, and recognition in images [3, 4]. The object detection is a technology that distinguishes and identifies a specific object from the background in visual data such as images and videos [5]. To this end, if CNN (Convolutional Neural Network) is used, regression and classification for object detection are possible. Efficient methods for object detection include the area proposal method and the method of finding objects with a predetermined location and size [6]. The region proposal method uses a RPN (Regional Proposal Network) that selectively searches for a region that is likely to contain the object. This has algorithms such as R-CNN, Fast-R-CNN, Faster-R-CNN, and the disadvantages of high accuracy but very slow processing speed. The method of finding an object of a predetermined location and a predetermined size predicts a fixed number of objects having a shape and a size in advance for each region. This is used in fields where real-time detection is required because there are algorithms such as YOLO (you only look once) and SSD (Single Shot Detector), and fast processing is possible [7, 8]. However, YOLO needs to be supplemented because fast processing is possible but the accuracy for classification is low. On the other hand, segmentation is a technique thatanalyzes an object in an image in pixels and clearly interprets the position of the object [9]. Segmentation is divided into semantic segmentation and instance segmentation [10; 11]. Semantic segmentation is a method of classifying all objects in pixels [10]. Instance segmentation is a method of classifying according to objects by adding object detection based on semantic segmentation [8]. Algorithms used for image segmentation include FCN (Fully Convolutional Network) and Mask-R-CNN etc. This has the disadvantage that the processing speed is very slow. Potholes are caused by factors such as climate change, traffic volume, road aging, and vehicle weight. In road damage, a pothole refers to a damaged road surface [12]. This may also cause traffic accidents. According to data from the Ministry of Land, Infrastructure and Transport, the number of road damage to potholes in Korea in 2019 amounted to 650,000 [13]. As a result, 654 human accidents, 2 deaths, and 5,153 material accidents, and 1 trillion won of road repair costs were counted. A pothole is a serious risk factor for the safety of road traffic drivers and fellow passengers and the stability of driving. Therefore, there is a need for a method that can detect road conditions at various locations and identify related information for efficient management and maintenance. There are various methods of current methods for detecting potholes include detection using an acceleration sensor, scanning using a laser, and detection using an image and etc. [14]. A detection method using an acceleration sensor is a disadvantage of low accuracy because there is a possibility to recognize even speed bumps [15]. The laser scan method is can measure general cracks as well as potholes, but the cost is very high [16]. In addition, a detection method using an image is a method that can detect a large area of potholes at a lower cost than the method using an acceleration sensor and a laser [17]. In images, however, it is difficult to detect potholes due to factors such as light and darkness and image quality. Therefore, there is a need for a pothole detection technology considering these factors. Therefore, this study proposes a pothole classification model using edge detection in road image.