A Review of Road Surface Anomaly Detection and Classification Systems Based on Vibration-Based Techniques

Road surfaces suffer from sources of deterioration, such as weather conditions, constant usage, loads, and the age infrastructure. These decay generate anomalies that could cause harm to vehicle users pedestrians also develop a high cost repair irregularities. drawbacks have motivated development systems automatically detect classify road anomalies. This study presents narrative review focused on surface anomaly detection classification based vibration-based techniques. Three methodologies were surveyed: threshold-based methods, feature extraction techniques, deep learning Furthermore, datasets, signals, preprocessing steps, techniques are presented. The results this show performed through methods achieved relatively performance. However, there challenges related reproduction heterogeneity been reported influenced by limited testing sample size, lack publicly available datasets. Finally, is potential standardize features computed time or frequency domains evaluate compare diverse set settings time-frequency used for signal representation.