# Review of Recent Automated Pothole-Detection Methods
## 3 The Three Types of Recent Automated Pothole-Detection Methods
### A Vision-Based Method (Type A)
The experimental results showed that the accuracy, precision, recall, and F1 score of the proposed method were 0.950, 0.952, 0.920, and 0.936, respectively. Authors explained that the proposed method had better performance than the existing methods [17; 18; 19; 20] through the experimental results. The method proposed in this paper has scalability in detecting not only potholes, but also bumps, cracks, or rutting. However, the proposed method has a disadvantage in that performance is degraded when the input data is low-resolution. The authors proposed exploiting scale selections and cascade techniques to improve the performance of pothole detection as a future work. The authors also plan to conduct additional experiments on various types of roads such as concrete roads and gravel roads, as well as asphalt roads. [PERSON] et al. [21] proposed a pothole-detection method based on CNN with an embedded vehicle prototype. The proposed model utilized a monocular camera unit and Raspberry Pi computing device for pothole detection. The system consisted of three modules: a pothole-detection module, a data-processing module, and an embedded autonomous-vehicle-system (AVS) module. The dataset that was used in the process of training in a pothole-detection module was composed of 3915 images. The dataset size was increased from 783 to 3915 through standard data-augmentation techniques such as flipping, blurring, warping, and rotation techniques. The performance of the proposed method was evaluated by accuracy, precision, recall, and F1 score using the public pothole dataset [16]. The experimental results showed that the accuracy, precision, recall, and F1 score of the proposed method were 0.9902, 0.9903, 0.9903, and 0.9833, respectively. The authors explained that the proposed method had better performance than the existing methods [16; 22; 23; 24; 25] through the experimental results. The proposed method showed good performance by optimizing the CNN architecture according to the hardware requirements and the purpose of pothole detection. It is judged that a real-time pothole-detection system using low-cost edge devices such as Jetson Nano can be implemented based on the research results of this paper. [PERSON] [22] proposed pothole-detection methods based on a machine-learning algorithm using images as input data. The proposed approach for pothole detection consists of three phases: image acquisition and feature extraction; dataset construction; and artificial-intelligence (AI) model training and prediction. The 200 asphalt pavement images were prepared and classified into 100 pothole images and 100 non-pothole images in the image-acquisition phase. Image-processing techniques, including the Gaussian filter, a steerable filter, and integral projection were utilized for extracting features of images in the image-acquisition and feature-extraction phase. In the phase of AI model training and prediction, the least-squares support-vector machine (LS-SVM) and the artificial neural network (ANN) were applied. The performance of two proposed models was evaluated by the classification accuracy rate (CAR) and the area under the curve (AUC). The experimental results showed that the CAR of the LS-SVM and ANN were 88.75% and 85.25%, respectively. The authors explained that both LS-SVM and ANN are applicable methods for pothole detection through experimental results. They also showed that the performance of LS-SVM was higher than that of ANN through performance comparison of the two models. The authors proposed improving the accuracy of pothole detection as a future work by applying other advanced AI models in the training and prediction phases. The authors also suggested estimating the size of potholes as a future work by applying advanced image-signal-processing techniques in the feature-extraction phase. The pothole dataset of [22] is available through e-mail contact with the author of this paper if readers need it. [PERSON] et al. [26] proposed an asphalt pavement pothole-detection and -segmentation method based on a wavelet energy field.