Integrative Approach for High-Speed Road Surface Monitoring: A Convergence of Robotics, Edge Computing, and Advanced Object Detection

Abstract

To ensure precise and real-time perception of high-speed roadway conditions and minimize the potential threats to traffic safety posed by road debris and defects, this study designed a real-time monitoring and early warning system for high-speed road surface anomalies. Initially, an autonomous mobile intelligent road inspection robot, mountable on highway guardrails, along with a corresponding cloud-based warning platform, was developed. Subsequently, an enhanced target detection algorithm, YOLOv5s-L-OTA, was proposed. Incorporating GSConv for lightweight improvements to standard convolutions and employing the optimal transport assignment for object detection (OTA) strategy, the algorithm’s robustness in multi-object label assignment was enhanced, significantly improving both model accuracy and processing speed. Ultimately, this refined algorithm was deployed on the intelligent inspection robot and validated in real-road environments. The experimental results demonstrated the algorithm’s effectiveness, significantly boosting the capability for real-time, precise detection of high-speed road surface anomalies, thereby ensuring highway safety and substantially reducing the risk of liability disputes and personal injuries.

Keywords

• traffic safety
• accident risk
• machine learning
• object detection
• deep neural networks
• smart cities