Accurate crack width measurement is essential for the condition assessment of reinforced concrete (RC) bridges, yet most unmanned aerial vehicle (UAV) inspections remain limited to pixel-level observations that cannot be converted into reliable metric units without an external scale reference. This paper presents a lightweight, drone-agnostic UAV-mounted metrology system that enables standards-aligned metric crack width measurement directly from inspection imagery. The payload integrates a focusable diffractive optical element (DOE) red laser that projects a cross pattern of known angular geometry, three TF-Luna time-of-flight (ToF) distance sensors, and an ESP-WROOM-32 microcontroller that provides dual-rate sampling, Bluetooth Low Energy (BLE) streaming, and on-board logging. A two-stage calibration links the synchronized distance measurements to the physical length of the projected cross, yielding an image-specific pixel-to-millimeter scale that is applied to pixel-level crack widths obtained from a vision-based segmentation pipeline. The system is field-deployed on the Puente Huamani Bridge in Pisco, Peru, where measurements of 39 cracks classified under AASHTO MBEI condition states are compared against independent manual measurements by six inspectors. The proposed system reduces measurement variability across all condition states (CS), lowering the average coefficient of variation from 0.36 to 0.10 for fine CS1 cracks, from 0.27 to 0.11 for CS2, and from 0.22 to 0.07 for CS3. Cross-platform adaptability is demonstrated through an additional deployment on a DJI Matrice 350 RTK at the Low Level Bridge in Edmonton, Canada. The results indicate that the system provides a practical, low-cost, and scalable solution for repeatable, standards-aligned UAV-based bridge crack assessment.

Reinforced Concrete (RC) bridges are prone to deterioration throughout their service life, which can compromise their structural integrity. Among the visible indicators of deterioration, cracks are particularly significant, underscoring the need for their early detection. Traditional inspections, primarily based on visual inspection, face challenges such as restricted access to certain bridge areas, complex logistics, and excessive operational costs and time. To address these challenges, the engineering community is working toward implementing more efficient technologies for bridge inspection. This paper proposes an automatic deep learning-based system for the segmentation and quantification of surface cracks in RC bridges using images captured by unmanned aerial vehicles (UAVs). The proposed framework combines patch-based and pixel-level segmentation using binary image classification and crack segmentation models. Additionally, an alternative method based on a laser module with a diffractive optical element (DOE) was implemented to obtain an automatic scale factor, enabling the conversion of pixel-level information into metric measurements for damage quantification. Experimental results show that the binary classification model achieved an F1-score of 0.93 on the test set, while the segmentation model achieved an F1-score of 0.83. Overall system performance yielded a coefficient of determination (R²) of 0.70 and an average inference time of 2.32 seconds per high-resolution image. Through integrating UAVs, lasers, and deep learning, the proposed approach offers an alternative for bridge inspections, reducing cost and time while supporting the rapid generation of preliminary reports.