## 3 Road Anomaly Detection and Classification Approaches through Vibration-Based Techniques
### Learning-Based and Feature Extraction Methods
The time-domain features were the mean, variance, standard deviation, integral square, root mean square, median, entropy, and range. The tested frequency-domain features were the spectrum energy, median frequency, mean power peak magnitude, minimum magnitude, and total power. Additionally, the authors tested the wavelet transform through a Daubechies 2 wavelet. [PERSON] et al. [26] presented a similar feature extraction process that also proposed to extract features in the time, frequency, and time-frequency domain representations. The extracted features in the time-domain and frequency-domain were used to train a random forest classifier that achieved an accuracy of 95.7%, a precision of 88.5%, and a recall of 75.00%. In addition, [PERSON] et al. [32] proposed to compute scale-invariant features from accelerometer signals. The methodology of this study was first to segment the road anomaly using a piecewise aggregate approximation method and then classify the anomaly by learning scale-invariant features by computing shapelets. [PERSON] et al. [36] worked with the vertical and lateral acceleration data to assess the condition of the road. The justification for working with the vertical and lateral data is to generate a system that can distinguish between benign anomalies and defects on the road. The features computed to generate the detection algorithm were the coefficient of variation applied to the vertical acceleration component. A second feature was to use the singular value decomposition and the coefficient of variation but applied to the lateral acceleration component. The classification was made with two one-class support vector machines with a reported accuracy of 97.5%. Similar to other studies reported in the literature, [PERSON] et al. [37] proposed to compute time and frequency domain features from both the accelerometer and gyroscope data and apply a support vector machine to classify the quality of manholes into three classes labeled as good, average, and poor. These labels represent the degree of subsidence; this study reported a mean accuracy of 84.40%. Furthermore, in Ref. [38], the authors proposed to detect surface road environments, such as cobblestones, flatlands, and transits, with a KNN and eight features derived from linear accelerations from the z and y axes and gyroscope data (i.e., roll and pitch angles) achieving an accuracy of 93.2%. Table 4 shows a summary of the studies that have used feature extraction and machine learning for road surface anomaly recognition and the performance reported in each reference work. One of the critical advantages of learning-based techniques in combination with feature extraction is that the computational cost can be lower since no transformation is required, as in the case of time-domain features. However, the feature extraction could vary depending on the representation or domain in which the features are extracted or which statistics are computed, as presented in Ref. [26]. Furthermore, it is complicated to know a priori whether the set of proposed features could be invariant between samples and also assure class discrimination. The above was also pointed out in Ref. [18]. According to [PERSON] et al. [32], one of the main drawbacks of time-domain and frequency-domain features is that the differences related to different classes of road anomalies are attributed to local signal segments rather than global features. This problem can be attributed to the noise and outliers present in the signal segments or due to shifting or scaling. The problem of shifting and scaling could also be counter with convolutional neural networks (CNNs) due to their ability to create invariant representations to translations and scaling from the input data [39]. This type of architecture used for vibration-based road surface anomaly detection and classification is presented in the next section.