Researchers create an automated computer vision system that tracks and measures bridge damage over time using routine drone inspections

Bridges naturally develop cracks, spalling, and water leakage as they age, making regular inspections essential. However, these inspections are often labor-intensive and costly. To address this, researchers in South Korea have developed an AI-powered computer vision framework. The system combines routine drone images, 3D reconstruction, and satellite positioning data to accurately track and measure structural damage over time. This enables more efficient bridge maintenance while supporting predictive management of aging infrastructure.

Image title: AI-Powered Long-Term Bridge Damage Monitoring Using Drone Inspections

Image caption: Researchers develop new AI based framework that allows long-term monitoring of bridge damage with less than 5% measurement error

Image credit: Assistant Professor Hyunjun Kim from Seoul National University of Science and Technology

License type: Original Content

Usage restrictions: Cannot be reused without permission

Bridges form an important part of the road systems. Monitoring them is essential for ensuring structural safety as overtime these bridges develop cracks, concrete spalling, and water leakage due to traffic loads, weather, and environmental exposure. Traditional visual inspections are often labor-intensive, costly and sometimes hazardous. Computer vision has made automated bridge inspections more practical. However, comparing damage captured months apart remains difficult because images are typically taken from different positions and viewing angles.

Now, researchers from Seoul National University of Science and Technology, South Korea, led by Assistant Professor Hyunjun Kim, developed an automated computer vision framework that tracks structural damage over time using drone images collected during routine bridge inspections. Their findings were made available online in Structural Health Monitoring on April 27, 2026.

This study enables long-term monitoring by combining artificial intelligence with three-dimensional (3D) bridge reconstruction. The 3D model of the bridge is built using images captured during the initial inspection. Images from subsequent inspections are then automatically aligned with this model using hierarchical localization and image clustering, allowing the same damage to be identified and compared over time even when photographs are taken from different camera angles or distances. Additionally, the framework uses Global Navigation Satellite System data to convert image measurements into real-world dimensions.

"Long-term structural monitoring requires more than simply detecting damage, it requires understanding how that damage evolves," says Dr. Kim. "Our framework allows engineers to visualize damage progression and measure its severity using images collected during routine inspections."

The system was validated over 120 days by monitoring an in-service prestressed concrete bridge using drone imagery. The framework succeeded in tracking the progression of cracks, spalling, and water leakage throughout the study period despite changes in camera viewpoint. It was able to measure the damaged areas with a maximum error of only 4.61% as compared to conventional manual measurements.

Unlike traditional inspection methods which could analyze damage only at a single point in time or which requires rebuilding 3D models for every inspection, this study suggests continuous monitoring of damage using a single reference model. This approach improves consistency while reducing the computational effort needed for long-term assessments. Although the current method is best suited for relatively flat structural components and its accuracy might differ on highly curved surfaces, it can report most bridge elements commonly encountered during routine inspections.

"We believe this framework can help engineers make more informed maintenance decisions and contribute to extending the service life of critical infrastructure." says Dr. Kim

As transportation agencies face aging infrastructure and increasing maintenance demands, researchers believe their study could support predictive maintenance strategies, improving public safety while reducing long-term inspection and repair costs. In future this technique could also be adapted to monitor other infrastructures such as tunnels, dams, and elevated rail systems.

Reference

Title of original paper: Long-term monitoring of damage progression using multi-view images from routine inspections of bridges

Journal: Structural Health Monitoring

DOI: 10.1177/14759217261443618

About Dr. Hyunjun Kim

Dr. Hyunjun Kim is an Assistant Professor in the Department of Civil Engineering at Seoul National University of Science and Technology, South Korea. His research focuses on developing smart technologies for the maintenance and management of civil infrastructure, with particular emphasis on computer vision-based visual inspection and system identification to improve the structural integrity, durability, and resilience of infrastructure systems. He earned his Ph.D. in Civil Engineering from Ulsan National Institute of Science and Technology (UNIST) in 2020 and completed postdoctoral research in the laboratory of Prof. Billie F. Spencer Jr. at the University of Illinois Urbana-Champaign.