Quantifying impacts on remote photogrammetric inspection using unmanned aerial vehicles

Abstract

Remote photogrammetric inspection is a Non-Destructive Testing method used to quantify surface integrity and detect external discontinuities. The mobility and size of an unmanned aerial vehicle (UAV) offer the flexibility to quickly deploy remote photogrammetric inspections for large-scale assets. In this paper, the results of a photogrammetric inspection are presented as a 3D profile, reconstructed from UAV captured images. Experiments were conducted indoors using a wind turbine blade section obtained from a recently decommissioned asset. The naturally occurring surface features representative of environmental wear were augmented with a small number of artificial features to aid in the visualisation of inspection quality. An autonomous UAV system for photogrammetric inspections is demonstrated and the influence of image parameters such as environmental light levels, motion blur and focal blur quantified in terms of their impact on the inspection accuracy. Over the range of parameter values studied, the poorest scenario was observed to cause a degradation in reconstruction error by a factor of 13 versus the optimal. Reconstruction quality when employing a laser range scanner to maintain standoff distance relative to the object during flight was also investigated. In this schema, the controller automatically generated a real-time adaptive flight path to follow the outer profile of the wind turbine blade and, consequently, demonstrated improved image quality during close-range inspection of an object with complex geometry. Inspection accuracy was quantified using the error of the photogrammetric reconstruction as compared to a model acquired using independent metrology equipment. While utilising the laser-based adaptive path, error in the reconstructed geometry was reduced by a factor of 2.7 versus a precomputed circular path. In the best case, the mean deviation was below 0.25 mm. Instances of wind turbine blade damage such as edge crushing, surface imperfections, early stage leading edge erosion were clearly observed in the textured 3D reconstruction profiles, indicating the utility of the successful inspection process. The results of this paper evaluate the impact of optical environmental effects on photogrammetric inspection accuracy, offering practical insight towards mitigation of negative effects.

High-Resolution Structure-from-Motion for Quantitative Measurement of Leading-Edge Roughness

“…Visual inspection has long been applied for condition-monitoring of wind turbine blades [10]. In recent years, unmanned aerial vehicles (UAV) have received increased interest for remote inspection of wind turbines [11][12][13][14][15][16] with a lower downtime compared to manual rope-access inspection. From 2D images captured by the UAV, deep learning methods [13,15] can be used for detecting damages and erosion on the blades.…”

Rough or Noisy? Metrics for Noise Estimation in SfM Reconstructions

“…This would also provide a better understanding on the generalization capabilities of the proposed metrics. We chose to test on wind turbine blade data, as this is an industrial inspection area which has began to use SfM for capturing information more and more and research is focused on ensuring the high quality of the reconstructions [ 49 ]. In addition, wind turbine blade data are hard to acquire, because of the requirements by blade manufacturers, that blades in use are not normally imaged.…”

A Novel Centralization Method for Pipe Image Stitching

“…An active pose estimation technique using a laser-profiler made the calculation of pose independent to the nature and quality of the image (SfM is heavily dependent on these [29], [30]). The experiments verified that the camera pose could be retrieved ±0.5 mm and ±0.5° error margin.…”