This study investigates the application of computer vision techniques in Structural Health Monitoring (SHM). The advantages of multiple synchronized camera setups in capturing and analyzing the dynamic behavior of bridges are researched. The proposed methodology encompasses approach, setup, and data analysis techniques, with the final scope of extracting modal parameters from videos of a vibrating bridge. An operational pedestrian footbridge forced by human-induced vibrations serves as a case study. The findings demonstrate that computer vision techniques employing a multiple synchronized camera approach offer a precise, cost-effective, efficient, and safe alternative to conventional SHM approaches for the dynamic identification of bridges.

The progressive aging of bridge networks raises the need for more frequent and effective structural inspections. Conventional inspection methods are often costly, laborintensive, and potentially hazardous, especially in difficult-to-access areas. Drones are emerging as a promising alternative, enabling safer and more efficient data collection while providing high-resolution results. Their use can be extended to post-disaster assessments and large-scale surveys, with further potential offered by swarms equipped with Non-Destructive Testing (NDT) sensors. When integrated with machine learning techniques, drone-based inspections support agile and low-cost Structural Health Monitoring (SHM) procedures. In addition, the adoption of 5G-and-Beyond communication networks facilitates real-time, remote operations beyond visual line of sight (BVLOS), while immersive technologies such as virtual and augmented reality enhance operator awareness and data interpretation. This paper introduces a framework for remote bridge inspection that combines drones and virtual reality in a unified system, integrating autonomous mission planning, high-speed data transmission, real-time inspection, and decision-support tools. The approach aims to increase inspection frequency, reduce operational risks, and improve the reliability of infrastructure management.