A Lightweight UAV-Mounted Metrology System for Standards-Aligned Metric Crack Width Measurement in Reinforced Concrete Bridges
Open Access
Abstract
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.Keywords
Funding Information
- University of Alberta
This publication has 43 references indexed in Scilit:
- UAV-based quantitative crack measurement for bridges integrating four-point laser metric calibration and mamba segmentationAutomation in Construction, 2026
- Drone-based bridge inspections: Current practices and future directionsAutomation in Construction, 2025
- Seismic assessment of bridges through structural health monitoring: a state-of-the-art reviewBulletin of Earthquake Engineering, 2023
- Combined impact of carbonation and crack width on the Chloride Penetration and Corrosion Resistance of Concrete StructuresCement and Concrete Composites, 2021
- Densely connected deep neural network considering connectivity of pixels for automatic crack detectionAutomation in Construction, 2020
- Framework for automated UAS-based structural condition assessment of bridgesAutomation in Construction, 2019
- Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniquesAutomation in Construction, 2018
- Crack detection using image processing: A critical review and analysisAlexandria Engineering Journal, 2018
- Bridge inspection: human performance, unmanned aerial systems and automationJournal of Civil Structural Health Monitoring, 2018
- A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructureAdvanced Engineering Informatics, 2015