Automating Traffic Monitoring with SHM Sensor Networks via Vision-Supervised Deep Learning

• URL: http://arxiv.org/abs/2506.19023v1
• Date: Mon, 23 Jun 2025 18:27:14 GMT
• Title: Automating Traffic Monitoring with SHM Sensor Networks via Vision-Supervised Deep Learning
• Authors: Hanshuo Wu, Xudong Jian, Christos Lataniotis, Cyprien Hoelzl, Eleni Chatzi, Yves Reuland,
• Abstract summary: Bridges, as critical components of civil infrastructure, are increasingly affected by deterioration.<n>Recent advances in deep learning have enabled progress toward continuous, automated monitoring.<n>We propose a fully automated deep-learning pipeline for continuous traffic monitoring using structural health monitoring (SHM) sensor networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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