# Review of Recent Automated Pothole-Detection Methods
## 3 The Three Types of Recent Automated Pothole-Detection Methods
### A Vision-Based Method (Type A)
The proposed method consists of two processes: pothole detection and pothole segmentation. In the process of pothole detection, the wavelet energy field of the asphalt image was constructed by morphological processing and geometric criteria. The detected pothole was segmented via Markov random-field model and the pothole edge was extracted accurately. The proposed method was trained and tested on 120 pavement images. The experimental results showed that the overall accuracy, precision, and recall of the proposed method were 0.867, 0.833, and 0.875, respectively. The authors also showed that the performance of the proposed method is better than that the of existing other methods [25; 27] in the aspects of pothole detection and segmentation through the performance comparison. The authors proposed applying a more effective mathematic model as a future work to improve the accuracy of pothole detection. [PERSON] et al. [28] proposed a pothole-detection and -localization method using pavement images as input data. The proposed method consists of two processes: pavement-image classification and pothole localization. The pavement images were classified as two groups: pothole and non-pothole images, using a bag-of-words (BoW) approach. The authors utilized scale-invariant feature transform (SIFT) features to establish the visual vocabulary of words. The support vector machine (SVM) was applied for training and testing of histograms of words about pavement images. A graph-cut segmentation scheme was also used for localizing the potholes in the labeled pothole images. The proposed method utilized a dataset of 120 pavement images created by [PERSON] et al. [27]. The dataset of [28] was not open to the public. The experimental results showed that the accuracy, precision, and recall of the proposed method were 0.957, 0.970, and 0.941, respectively. The authors explained that the performance of a proposed method is better than the performance of existing other methods [22; 25; 27; 29] through the experimental results. The authors suggested applying the state-of-the-art texture-based techniques to improve the performance of pothole detection as a future work. The authors also proposed conducting additional experiments to detect other distresses such as cracks, depressions, rutting, and patching as a future work. Pothole-detection techniques of vision-based methods are summarized as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Authors** & \multicolumn{2}{c}{**Detection Techniques**} & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Performance**} & \multicolumn{2}{c}{**Experimental Circumstance**} \\ \hline A8 & [PERSON] et al. [25] & \(\bullet\) & Public detection: the wavelet energy field is constructed by morphological processing, geometric extraction & - & Total dataset: 123 images & \(\bullet\) & Accuracy: 0.867 & \(\bullet\) & MATLAB prototype \\  & & & Probe segmentation: Markov random & - & Training dataset: 30 images & \(\bullet\) & Precision: 0.833 & \(\bullet\) & MATLAB prototype \\  & & & Reflex & - & Testing dataset: 90 images & \(\bullet\) & Recall: 0.875 & \(\bullet\) & Recall: 0.875 \\ \hline A9 & \multirow{4}{*}{[PERSON]} & \(\bullet\) & Image classification: a bag-of-words (HOH) approach & \(\bullet\) & Total dataset: 123 images & \(\bullet\) & Accuracy: 0.957 & \(\bullet\) & \(\bullet\) \\  & & Preibble localization & - & Training dataset: 50 images & \(\bullet\) & Precision: 0.970 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\  & & - & Feature transformation (SIFT) & - & Testing dataset: 70 images & \(\bullet\) & Recall: 0.941 & \(\bullet\) & \(\bullet\) \\  & & - & Training and testing & Super & & & & \\  & & & Vector Machine (SVM) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Conf.