# Review of Recent Automated Pothole-Detection Methods
## 4 Conclusions  
Automated pothole-detection technologies will reduce the labor for managing potholes and make a great contribution to planned road management. They are identified as a key element of autonomous technology, and research on this technology is expected to continue. According to the development trend of technology, it is considered that research on automated pothole detection should be continued. In this paper, the methods proposed in the latest research for each of the three types of automated pothole detection are analyzed and their characteristics are described. The process and results of recent automated pothole-detection research based on each method are also summarized. Feature extraction and training and testing play an important role in vision-based methods. Image-processing technologies such as edge detection and SIFT are applied in the process of feature extraction in those methods. Deep-learning technologies such as CNN, YOLO, and SVM are used in the process of training and testing in those methods. Vibration-based methods generally consist of three steps, namely data preprocessing, feature extraction, and classification. Signal-processing techniques such as filtering, Fourier transform, and correlation are applied in the process of data preprocessing and feature extraction. Machine-learning techniques such as k-nearest neighbor, linear regression, and random forest are used in the process of classification. 3D reconstruction-based methods that are presented in this paper include the process such as data processing and training and testing. Signal-processing and deep-learning techniques such as filtering, PCA, and a modified U-Net are applied in those methods. The performance of the proposed models based on the three methods is commonly evaluated for accuracy, precision, and recall. The weblink about the publicly available pothole datasets is provided for reader's future research. The automated pothole-detection methods presented in this paper are expected to be applicable in various fields such as road management in connection with the intelligent transportation system. It can be effectively used in the establishment of preventive road-maintenance policies by sharing the various feature information obtained in the pothole-detection process and performing big-data analysis. It is expected to make a great contribution to improving the performance of vehicle suspension systems by analyzing the pothole-detection results and the acceleration-sensor data. Furthermore, it is expected to greatly contribute to improving the performance of autonomous-driving technology by combining the results of pothole detection with real-time traffic information, which is provided by the intelligent transportation system. In the course of the development of automated pothole detection, the application of deep-learning and machine-learning technology is gradually expanding. In particular, many researchers are working to improve the accuracy of pothole detection while applying various algorithms based on deep learning and machine learning [41]. In addition, research on edge devices capable of real-time pothole detection is being actively conducted. It is judged that improvements in pothole-detection accuracy and real-time pothole detection will become an important axis in future research trends. The application of common indicators such as IoU, mAP is required for objective accuracy evaluation when deep-learning and machine-learning techniques are applied in the pothole-detection process [42; 43]. It is judged that the development and application of these common indicators will become the other important axis in future research trends. We plan to implement an edge device capable of real-time pothole detection using a public pothole dataset in the future. We also plan to secure the objectivity of the research by performing an accuracy evaluation using common indicators for the implemented system. Y.-M.K. : Conceptualization, data curation, formal analysis, and writing--original draft preparation; Y.-G.K. and S.-Y.S.