Rapid Non-Contact Road Surface Inspection Using Tire-Road Coupling Noise and MFCC feature

Abstract

Accurate and efficient inspection of road surface defects is essential for ensuring traffic safety and supporting timely maintenance. However, existing vision-based inspection techniques often rely on high-resolution cameras or laser sensors, which limit robustness under complex or dynamic environmental conditions. To overcome these challenges, this study develops a vehicle-mounted acoustic inspection framework based on tire–road coupling noise for rapid road surface condition assessment. An acoustic inspection dataset is established, covering multiple types of urban pavement defects under diverse traffic and environmental conditions. The captured noise signals are transformed into MFCC, which serve as frequency-domain features for defect detection. A deep neural network integrating CNN, SE attention, and BiLSTM modules is developed to extract multi-scale time-frequency features and model temporal dependencies from tire-road coupling noise for pavement defect detection. Model evaluation results demonstrate that the proposed CNN-SE-BiLSTM model can accurately classify pavement defects without reliance on visual sensors, achieving an overall F1-score of 84.0%, while maintaining real-time inference and strong robustness to varying road surface conditions. Compared with existing vision- and Sensor- based inspection methods, the proposed method offers advantages in lower deployment cost, reduced sensitivity to illumination and weather variations, and easier integration into standard vehicles for continuous large-scale inspection. A field experiment on urban roads verifies the effectiveness of the proposed road surface inspection method, and a total of 12 pavement defects, including 9 cracks and 3 potholes, were successfully identified under real driving conditions. It is concluded that the proposed tire-road coupling noise method provides a cost-effective solution for road surface inspection.