# Review of Recent Automated Pothole-Detection Methods
## 3 The Three Types of Recent Automated Pothole-Detection Methods
### Vibration-Based Method (Type B)  
[PERSON] et al. [2020] proposed a road-surface-recognition method based on smartphone acceleration sensor when the vehicle passes through an abnormal road surface. The proposed method consists of three phases: data acquisition and preprocessing, abnormal-road-surface recognition, and abnormal-surface classification. The vehicle speed, acceleration, and position information were collected by the smartphone's built-in accelerometer and Global Positioning Navigation system. The raw data were preprocessed using a Butterworth filter. The improved Gaussian model was used to recognize the abnormal road surface using the z-axis acceleration threshold condition. The training samples and the test samples were collected on two different roads for maintaining independence. The k-nearest neighbor (kNN) algorithm was used to classify the abnormal pavement types, including potholes and bumps. The authors measured the accuracy by comparing the field-measurement results for abnormal road surfaces such as potholes and bumps with the identification results of the proposed method. The test result shows that the accuracy of the recognition of the road-surface pothole is 0.9603, and the accuracy of the road-surface bump is 0.9412. [PERSON] et al. [2020] proposed an automated pothole-detection method based on accelerometer and gyroscope built in a smartphone using the machine-learning technique. The proposed method consists of two main phases: a training phase and a prediction phase. The training phase consists of four steps: data collection, data preprocessing, features extraction and selection, and training a classifier. The raw data were collected using an accelerometer and gyroscope sensor built in the smartphone, and they were preprocessed by applying a low pass filter. The preprocessed data in the time domain were transformed the Fourier coefficients in the frequency domain using Fourier transform for feature extraction. The effective feature data were selected from the frequency and time-domain feature data using a correlation-based feature selection. The selected feature data were used to train a classifier model for identifying road conditions. A machine-learning technique, such as C4.5 decision tree, support vector machine (SVM), and Naive Bayes was used as classification algorithm. The subprocesses of the prediction phase are the same as the subprocesses of the training phase. The prediction of the road quality was performed by loading the classifier model, which was acquired in the training phase. The performance of the three algorithms used as classifier in the proposed method was compared using classification accuracy, precision, and recall. The experimental results showed that the performance of the C4.5 decision tree was better than the performance of support vector machine (SVM) and Naive Bayes. [PERSON] et al. [2020] proposed an automated pothole-detection method based on accelerometer and Global Positioning System receivers embedded in smartphones. The proposed method consists of four phases: data acquisition, data processing, feature extraction, and classification. The data-acquisition process was conducted online using a smartphone; other processes were conducted offline using a laptop. The vibration information was collected by using accelerometer and Global Positioning System of a smartphone in the vehicle. Data processing was performed to obtain useful features for pothole detection using sliding windows with simple threshold methods. Features were extracted from potential pothole windows in the time and frequency domain. Finally, machine-learning classifiers such as linear regression (LR), support vector machine (SVM), and random forest (RF) were applied for training and testing the dataset. The performance of the proposed method was evaluated by precision, recall, F1-score, and accuracy. The experimental results showed that the features from the time and frequency domain were better than other features for detecting a pothole.