“Deep Learning for Vision-Based Bridge Inspection by Resource-Constrained Unmanned Aircraft Systems”

Bridge inspection is critical for ensuring structural integrity, extending the service life of infrastructure, and minimizing maintenance costs. As bridges age and endure increasing loads, regular inspections help detect early signs of wear, such as cracks or corrosion, that could impact safety and performance. However, traditional inspection methods are labor-intensive, requiring significant time, specialized equipment, and manual access to challenging areas, which can lead to costly disruptions. Additionally, reliance on human inspectors introduces subjectivity, with assessments varying by individual expertise. These factors highlight the inefficiencies and safety risks in current inspection practices, underscoring the need for more objective, efficient solutions.

In this work, we propose advanced computer vision techniques, leveraging deep learning, to streamline and enhance the bridge inspection process. The vision techniques are specifically designed to work onboard small, Group 1 Unmanned Aircraft Systems (UASs) and hence are SWaP (Size, Weight, and Power) constrained friendly. Specifically, we address challenges in detecting and assessing defects in both concrete and steel bridge components under real-world outdoor conditions. For concrete bridges, we construct robust datasets capturing transverse surface cracks and design a deep learning segmentation model for crack width measurement. For steel bridge inspection, we examined object detection algorithms focused on critical connection components like rivets and bolts, emphasizing computational efficiency and precision to deploy the model for SWaP-constrained devices. Additionally, a novel classification approach is presented to evaluate corrosion severity in steel structures, offering an automated solution for tracking progressive structural deterioration.

Through these contributions, this thesis advances the field of autonomous bridge inspection by integrating scalable deep learning methodologies into Group 1 UAS, providing a practical and transformative approach to structural health monitoring that supports safer, more cost-effective, and data-driven infrastructure maintenance.

Committee:
Dr. Justin Bradley, Co-chair
Dr. Chungwook Sim, Co-Chair
Dr. Carrick Detweiler
Dr. Armin Roohi
Dr. Daniel Linzell

Zoom: https://unl.zoom.us/j/98957381254?pwd=ysfwO8muXwoNhpqvdYGZJjahCaSgBr.1