Automating Traffic Monitoring with SHM Sensor Networks via Vision-Supervised Deep Learning
This addresses the problem of reliable and automated traffic load monitoring for civil infrastructure maintenance, offering a solution that overcomes privacy and lighting limitations of vision-based methods, though it is incremental as it builds on existing deep learning and sensor technologies.
The paper tackles automated traffic monitoring for bridge health by proposing a deep learning pipeline that integrates computer vision with structural health monitoring sensor networks, achieving state-of-the-art classification accuracies of 99% for light vehicles and 94% for heavy vehicles in a real-world case study.
Bridges, as critical components of civil infrastructure, are increasingly affected by deterioration, making reliable traffic monitoring essential for assessing their remaining service life. Among operational loads, traffic load plays a pivotal role, and recent advances in deep learning - particularly in computer vision (CV) - have enabled progress toward continuous, automated monitoring. However, CV-based approaches suffer from limitations, including privacy concerns and sensitivity to lighting conditions, while traditional non-vision-based methods often lack flexibility in deployment and validation. To bridge this gap, we propose a fully automated deep-learning pipeline for continuous traffic monitoring using structural health monitoring (SHM) sensor networks. Our approach integrates CV-assisted high-resolution dataset generation with supervised training and inference, leveraging graph neural networks (GNNs) to capture the spatial structure and interdependence of sensor data. By transferring knowledge from CV outputs to SHM sensors, the proposed framework enables sensor networks to achieve comparable accuracy of vision-based systems, with minimal human intervention. Applied to accelerometer and strain gauge data in a real-world case study, the model achieves state-of-the-art performance, with classification accuracies of 99% for light vehicles and 94% for heavy vehicles.