## 3 Road Anomaly Detection and Classification Approaches through Vibration-Based Techniques
### Learning-Based and Feature Extraction Methods  
Some studies have opted to extract features from the accelerometer or gyroscope data by extracting features in the time-domain or frequency-domain (i.e., transforming the signal through the Fourier Transform (FT)) for road surface anomaly detection and classification. The above is done to input the extracted features into machine learning techniques. Figure 4 shows the flow of activities to apply learning-based techniques [33]. The first step consists in collecting the dataset. The second step is related to the preprocessing steps performed on the dataset, such as outliers detection, missing values handling, sensor data reorientation, re-sampling, and segmentation. The third step is related to the feature extraction process performed on the data. Finally, the last steps are related to the model generation and validation stages. In these two last stages, there is an iterative process in which different models are tested and validated since it is complicated to find a priori a model that represents all reality according to the No Free Lunch theorem [34]. Figure 4: General methodology to apply machine learning or deep learning techniques for classification tasks. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Year** & **Classified Road Anomalies** & **Algorithm** & **Reported Performance Metrics** \\ \hline [PERSON] et al. [14] & 2018 & \begin{tabular}{c} Potholes \\ Bumps \\ Metal bumps \\ \end{tabular} & \begin{tabular}{c} STDEV(Z) \\ threshold \\ \end{tabular} & Average F1-score: 74.40\% \\ \hline [PERSON] et al. [22] & 2019 & \begin{tabular}{c} Potholes \\ (Z-THRESH) \\ \end{tabular} & \begin{tabular}{c} Grubss Test \\ and threshold \\ (Z-THRESH) \\ \end{tabular} & Precision-Recall curves graphs. \\ \hline [PERSON] et al. [21] & 2020 & \begin{tabular}{c} Pothole \\ Speed bump \\ Metal bump \\ \end{tabular} & \begin{tabular}{c} Query filter plus \\ self-similarity \\ \end{tabular} & \begin{tabular}{c} F1-score: greater than 70\% for \\ potholes, speed bumps, \\ and metal bump. \\ \end{tabular} \\ \hline [PERSON] et al. [23] & 2020 & \begin{tabular}{c} Pothole \\ Speed bump \\ Metal bump \\ \end{tabular} & \begin{tabular}{c} Threshold in \\ combination with \\ Random Forest and \\ Dynamic Time Warping \\ \end{tabular} & \begin{tabular}{c} F1-score: 93.90\% for pothole \\ F1-score: 87.4 \% for speed bump \\ F1-score: 81.9\% for metal bump \\ \end{tabular} \\ \hline [PERSON] et al. [11] & 2021 & \
\begin{tabular}{c} Potholes \\ Manholes \\ Cracks \\ Road joints \\ \end{tabular} &
\begin{tabular}{c} Hybrid approach \\ Threshold plus \\ Gaussian Mixture Model \\ \end{tabular} & Accuracy: 70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Methods used for road anomaly detection and classification based on threshold techniques. Examples of studies that have performed this process are shown next. For instance, in Ref. [35], the data from accelerometers were used to identify potholes, speed bumps, straight roads, and curve roads by employing the power spectrum of the signal using the Fast Fourier Transform (FFT). In this case, the learning was completed with a k-nearest neighbor (KNN) and a multilayer perceptron with four hidden layers. The authors reported an accuracy of 95.55% for the KNN and 96.79% for the multilayer perceptron. Additionally, [PERSON] et al. [5] proposed to extract features from accelerometric data, such as the mean, variance, standard deviation, skewness, kurtosis, the minimum value, the maximum value, and dynamic range to detect speed bumps. The results of this study reported an accuracy of 97.14% by employing a logistic regression and finding the optimal coefficients of the logistic model through a genetic algorithm. Similar research was conducted by [PERSON] et al. [18], who explored the features of the time and frequency domains for road monitoring by testing a support vector machine, a decision tree, and a multilayer perceptron.