# Review of Recent Automated Pothole-Detection Methods
## 1 Introduction  
A pothole is a hole in a road surface that results from gradual damage caused by a traffic and weather conditions. Potholes, a kind of road defect, can damage vehicles and negatively affect drivers' safe driving, and in severe cases can lead to traffic accidents.  
According to the Korea Express Corporation, the number of potholes identified on Korean expressways from 2015 to 2019 was approximately 47,200. Considering the total distance of 4767 km of expressways in South Korea, the average number of potholes per kilometer of expressways is about 10. Additionally, 221 whole-related traffic accidents were reported officially from 2017 to 2019 [1]. Identifying and managing potholes in advance plays an important role in securing driver safety and preventing traffic accidents.  
The general process of pothole detection consists of four steps: data acquisition, data preprocessing, feature extraction, and pothole classification. The data-acquisition step is the process of building a dataset for learning or analyzing potholes by acquiring raw data such as various sensor data or images. In the data-acquisition step, it is important to obtain sufficient data to learn and analyze the pothole. The data-preprocessing step uses the dataset built in the previous step to refine the data for easy learning or analysis of potholes. In the data-preprocessing step, a variety of signal-processing techniques such as filtering and masking are applied to refine the raw data. The feature-extraction step is a process of finding a factor to distinguish between potholes and non-potholes in the preprocessed data. In the feature-extraction step, it is important to consider the type and characteristicsof the preprocessed data when looking for factors for pothole classification. The pothole-classification step determines the existence of potholes by applying a pothole-detection algorithm based on the features. In the pothole-classification step, various algorithms are applied to improve the accuracy of pothole classification and an optimal method is derived in the process. In the pothole-detection process, data sensing and processing is an important factor that determines the type of pothole detection and affects its performance. Therefore, the pothole-detection technique based on data sensing and processing is considered to be of great value compared to other possible techniques. The general process of pothole detection and considerations in each step are summarized in Figure 1.  
In the past, pothole detection was mainly performed by visual inspection by human experts. Recently, automated pothole-detection methods using various techniques have been applied [2; 3; 4; 5; 6; 7; 8]. Automated pothole-detection methods can be classified into three types according to the technology used in the pothole-recognition process. The three automated pothole-detection methods are a vision-based method, a vibration-based method, and a 3D reconstruction-based method. The three methods summarized in this paper were selected based on the most widely applied method through the results of recent research trend analysis.  
This paper is structured as follows. Three automated pothole-detection methods were compared, and the strengths and weaknesses of each method are summarized in Section 2. The detection process and technology proposed in the latest research related to three types of automated pothole detection are described for each method in Section 3. The performance and characteristics of recent automated pothole detection research are also summarized in Section 3. Conclusions and future work are presented in Section 4.