A multimodal approach to road condition monitoring for enhanced infrastructure management

The maintenance and management of road infrastructure are critical for ensuring public safety, reducing transportation costs, and enhancing the overall efficiency of transportation networks. Traditional methods of road condition monitoring often rely on manual inspections or single-mode data collection techniques, which can be time-consuming, labor-intensive, and prone to human error. This study proposes a multimodal approach to road condition monitoring, integrating multiple data sources and advanced technologies to provide a more comprehensive and accurate assessment of road conditions. By leveraging data from inertial measurement unit (IMU) sensors, GPS tracking, cameras, and machine learning algorithms, this approach aims to enhance infrastructure management, enabling more proactive and cost-effective maintenance strategies. The system effectively detects road defects by integrating sensor data with computer vision, providing real-time processing and accurate mapping of road anomalies for targeted maintenance.