## 3 Road Anomaly Detection and Classification Approaches through Vibration-Based Techniques
### Deep Learning-Based Methods  
Deep learning techniques that have been used for road surface anomaly detection and classification are deep feedforward networks (DFN), CNNs, recurrent neural networks (RNNs), and long-short term memories (LSTMs) neural networks. These techniques have the main advantage of not requiring a feature engineering process since the algorithms can handle the raw data without needing any signal transformation or representation. Hence, the methodology presented in Figure 4 does not have a separate feature extraction stage since this process is performed during the model generation stage. For example, [PERSON] et al. [1] proposed to automatically identify potholes and destabilizations produced by speed bumps or driver actions by comparing CNNs and LSTMs by processing the accelerometer data from smartphones. [PERSON] et al. [40] proposed to use time-frequency representations from inertial sensors to train CNNs to detect and classify road anomalies reporting an accuracy of 97.2%. The time-frequency representations tested were the short-time Fourier Transform (STFT) and the continuous wavelet transform (CWT). In addition, [PERSON] et al. [3] compared DFNs, CNNs, and RNNs to identify eight pavement anomalies based on processing inertial sensors, spindle, and shock signals. This study showed that the RNNs performed better than the DFNs and CNNs with fewer parameters. Furthermore, [PERSON] et al. [41] proposed a CNN for the road surface quality assessment and considered \begin{table}
\begin{tabular}{p{113.8 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** & **Performance Metrics** \\ \hline [PERSON] et al. [5] & 2018 & Speed bump & Logistic Regression & Accuracy: 97.14\% \\ \hline [PERSON] et al. [36] & 2019 & Benign anomalies Defect of the road Machine & One-class Support Vector Machine & Accuracy: 97.50\% \\ \hline [PERSON] et al. [26] & 2020 & Potholes & Random Forest & Accuracy: 95.7\% \\ \hline [PERSON] et al. [37] & 2022 & Manholes & Support Vector Machine & Accuracy: 84.40\% \\ \hline [PERSON] et al. [35] & 2022 & Pothole Speed bump Curve & k-Nearest Neighbor & Accuracy: 95.55\% Plain & Accuracy: 95.55\% \\ \hline [PERSON] et al. [18] & 2022 & Potholes Metal bumps Asphal bumps Worn out roads & Decision Tree & Accuracy: 94.00\% \\ \hline [PERSON] et al. [38] & 2022 & Cobblestones Flatlands Transits & k-Nearest Neighbor & Accuracy: 93.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Methods used for road anomaly detection and classification based on feature extraction techniques. it as input accelerometer data. The proposal achieved a performance of 98.5% in terms of precision compared to neural feedforward networks and support vector machines. Other studies have aimed to compare feature extraction approaches with deep learning techniques. An example is the one presented by [PERSON] et al. [42] that compared the use of decision trees and support vector machines with features extracted from accelerometer and gyroscope data with the input of raw data into a multilayer perceptron architecture to detect and classify smooth roads, potholes, and deep transverse cracks. The previous study used the three axes of the sensors instead of using only one single axis of the data, such as previous works [29]. Likewise, [PERSON] et al. [43] used inertial sensor datasets collected in different contexts to detect and classify surface road anomalies, such as dirt, cobblestone, and asphalt roads, by comparing classical machine learning techniques and deep learning techniques. Based on the results reported by the authors, it was observed that a CNN achieved the best performance with an accuracy of 93.17% compared to an LSTM and a gate recurrent unit. Finally, the study of [PERSON] et al. [44] developed a system focused on detecting road anomalies and determining the classification of unpaved road types. The algorithm used to perform the detection was a Spiking Neural Network originally proposed by [PERSON] et al.