# Review of Recent Automated Pothole-Detection Methods
## 3 The Three Types of Recent Automated Pothole-Detection Methods  
### A Vision-Based Method (Type A)  
[PERSON] et al. [9] proposed automated pothole-detection methods based on deep-learning techniques using images as input data. Authors of [9] collected pothole images through public database sites such as Flicker, Google Images, and Pixabay. They labeled and annotated a bounding box representing the size and position of the pothole in each image. The dataset consists of 996 training images containing 1796 potholes and 203 testing images. The dataset on which labeling and annotation was performed based on pothole images collected by the authors of the paper was not open to the public. The proposed methods for training and testing the pothole dataset in the paper were two modified models based on \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & & **Strengths** & & **Weaknesses** \\ \hline \multirow{4}{*}{Vision-based method} & • • It is more cost-effective than the 3D & • • It has limitations in measuring information \\  & • reconstruction-based method & • It is suitable for determining the number and & • It is affected by lighting and \\  & • approximate shape of potholes & • shadow condition \\ \hline \multirow{4}{*}{Vibration-based method} & • It is the most cost-effective among the three methods & • It has limitations in providing the exact shape \\  & • It requires small storage & • It is affected by the sensor and vehicle \\  & • Real-time data processing can be applied & • applied in the data-acquisition process \\ \hline
3D reconstruction-based method & • It measures the shape of potholes most accurately among the three methods & • It is the most expensive among the three methods \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The strengths and weaknesses of automated pothole-detection methods. Figure 2: The characteristics of automated pothole-detection methods. YOLOv2 (You Only Look Once version 2). In the design of the two modified models, the creation of an anchor box suitable for pothole detection and the application of the denser grid suitable for input-image resolution were considered. The proposed models are an anchor-box model based on YOLOv2 and a denser-grid model based on YOLOv2. Authors evaluated the performance of two proposed models and original YOLOv2 using average precision, recall and frames per second (FPS) as evaluation indicators. They also explained that the proposed models were superior to YOLOv2 through the performance evaluation result. The two proposed models were able to exhibit superior performance compared to original YOLOv2 by reducing the layers of the YOLOv2 and adjusting the size of the anchor box. However, it was difficult to apply it directly to the pothole-detection process of a vehicle since this study was trained using a limited number of pothole data. In this respect, authors proposed real-time pothole detection as a future work by training more pothole data. [PERSON] et al. [10] proposed a pothole-detection and -classification method based on edge detection using pavement images as input data. The proposed method consists of three phases: image preprocessing, feature extraction of road damage, and road-damage classification. In the process of image preprocessing, RGB image data were converted to gray-scale image data, and the objects in images except potholes were detected via object-detection algorithm. The contour of the pothole in the preprocessed images was extracted via edge-detection algorithm for feature extraction. Potholes were detected and classified via YOLO algorithm in the road-damage-classification phase. The performance of the proposed method was evaluated by distortion rate and restoration rate of the image, and the accuracy of the classification. The dataset used in the performance evaluation process is the Global Road Damage Detection Challenge 2020 dataset [11]. The experimental results showed that the mean-squared error (MSE) of the distortion rate and restoration rate of the proposed method had errors of 0.2-0.44.