## 6 Discussion  
As can be seen in Table 3 threshold-based techniques have been developed recently to perform road surface anomaly detection and classification. However, recent approaches have combined thresholds with statistical tests or learning techniques [11; 14; 21]. Another aspect to point out is that the metrics commonly reported are not homogeneous among the studies, making it difficult to compare them. At the same time, Table 4 shows the results of the studies using feature extraction techniques with machine learning techniques. In this case, it can be seen that there is no preference for a particular machine learning technique, and most of the studies show accuracies greater than 80% regardless of the feature engineering method and the machine learning techniques that were selected. However, it is complicated to compare the studies since each listed author generated datasets in different scenarios or conditions. In addition, some studies focused on specific road anomalies or considered different anomalies to develop their respective systems. Finally, Table 5 shows the studies that used deep learning techniques. In this case, CNNs have been more frequent than RNNs. Finally, deep learning has shown a performance more outstanding than 90% in terms of accuracy. Nevertheless, similar to the studies that employed feature extraction, it is difficult to make a homogeneous comparison between the methodologies since different datasets, road scenarios, or anomalies have been analyzed. Table 9 shows an overview of the advantages and disadvantages of vibration-based techniques used in road surface anomaly detection and classification tasks. The metrics reported are another factor that mitigates a homogeneous comparison between the current proposals. For instance, the feature extraction and deep learning approaches focus their results on the accuracy, as shown in Tables 4 and 5. On the other hand, threshold-based techniques have focused on metrics, such as the F1-score. One of the main drawbacks of accuracy and F1-score is that these metrics overlook the individual class or anomaly detection capability better represented by other metrics, such as sensitivity or specificity. However, by reporting these metrics, the comparison is still complicated due to the different anomalies analyzed in each work. Moreover, in most of the reference studies, validation strategies such as cross-validation or bootstrapping have not been extensively used in the literature to demonstrate the performance of algorithms with different training or testing sets [72]. One of the main problems that learning-based techniques have is that whether the authors decide to employ a feature extraction technique or deep learning architectures, these \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{**Threshold-based**} & \(\bullet\) It does not require a training process. & \(\bullet\) Threshold are set empirically. \\  & \(\bullet\) Less computational costly compared to & \(\bullet\) It requires calibration of \\  & machine learning techniques. & \(\bullet\) It is susceptible to noise. \\ \hline \multirow{3}{*}{**Feature Extraction**} & \(\bullet\) Less computational costly compared to & \(\bullet\) It requires of a high quality dataset. \\  & deep learning algorithms. & \(\bullet\) Its feature extraction process is not \\  & \(\bullet\) The models are less complex & \(\bullet\) It requires a large sample size. \\  & compared to deep learning solutions. & \(\bullet\) It lacks of interpretability. \\ \hline \multirow{3}{*}{**Deep Learning**} & \(\bullet\) Can achieve relatively high accuracy. & \(\bullet\) It requires a large sample size. \\  & \(\bullet\) It does not require a feature & \(\bullet\) It requires large training times. \\ \cline{1-1}  & extraction process. & \(\bullet\) It has a high computational cost. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Advantages and disadvantages of road anomaly detection and classification systems through vibration-based techniques.