Drones for inspection: the evidence
What the literature measures about UAV inspection of bridges, tanks, sewers and structures: accuracy against manual methods, time, safety exposure, and where a drone still does not reach.
10 works. Metadata and abstracts from OpenAlex, harvested 2026-08-26. We have not summarized, graded or characterized this research ourselves, and the abstracts below are the authors' own.
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference...
Drone-enabled bridge inspection methodology and application
Image-based crack assessment of bridge piers using unmanned aerial vehicles and three-dimensional scene reconstruction
Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection
Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle
Bridge inspection using unmanned aerial vehicles (UAV) with high performance vision sensors has received considerable attention due to its safety and reliability. As bridges become obsolete, the number of bridges that need to be inspected increases, and they require much maintenance cost. Therefore, a bridge inspection method based on UAV with vision sensors is proposed as one of the promising strategies to maintain bridges. In this paper, a crack identification method by using a commercial UAV with a high resolution vision sensor is investigated in an aging concrete bridge. First, a point cloud-based background model is generated in the preliminary flight. Then, cracks on the structural surface are detected with the deep learning algorithm, and their thickness and length are calculated. In the deep learning method, region with convolutional neural networks (R-CNN)-based transfer...
Feasibility of using digital image correlation for unmanned aerial vehicle structural health monitoring of bridges
Quantifying the condition of aging structures is important to verify structural integrity and long-term reliability. Structural health monitoring plays a key role in the prevention of catastrophic failure, in improving the safety of infrastructure, and in reducing the downtime and costs associated with their maintenance. Bridges are typically designed to have a lifespan on order of 50 years; therefore, bridge monitoring is important since many of them are near to or have already exceeded their design life. Conventional sensors and examination techniques such as accelerometers and strain gages produce results at only a discrete number of points. Visual inspection only provides qualitative information and is subject to human variability and inconsistencies between inspectors. Moreover, both approaches are labor intensive and time-consuming. In recent years, three-dimensional digital image...
Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis
Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can...
Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling
Unmanned aerial vehicles (UAV) are now a viable option for augmenting bridge inspections. Utilising an integrated combination of a UAV and computer vision can decrease costs, expedite inspections and facilitate bridge access. Any such inspection must consider the design of the UAV, the choice of cameras, data acquisition, geometrical resolution, safety regulations and pilot protocols. The Placer River Trail Bridge in Alaska recently served as a test bed for a UAV inspection methodology that integrates these considerations. The end goal was to produce a three-dimensional (3D) model of the bridge using UAV-captured images and a hierarchical Dense Structure-from-Motion algorithm. To maximise the quality of the model and its benefits to inspectors, this goal guided UAV design and mission planning. The resulting inspection methodology integrates UAV design, data capture and data analysis...
LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection
UAV Bridge Inspection through Evaluated 3D Reconstructions
Imagery-based, three-dimensional (3D) reconstruction from unmanned aerial vehicles (UAVs) holds the potential to provide safer, more economical, and less disruptive bridge inspection. In support of those efforts, this paper proposes a process using an imagery-based point cloud. First, a bridge inspection procedure is introduced, including data acquisition, 3D reconstruction, data quality evaluation, and subsequent damage detection. Next, evaluation mechanisms are proposed including checking data coverage, analyzing point distribution, assessing outlier noise, and measuring geometric accuracy. The overall approach is illustrated in the form of a case study with a low-cost UAV. Areas of particular coverage difficulty involved slim features such as railings, in which obtaining sufficient features for image matching proved challenging. Shadowing and large tilt angles hid or weakened...
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