Wear Quantification of Wind Turbine Blades by Drone-Captured High-Resolution Images

Erosion of wind turbine blades, caused by the continuous impact of airborne particles such as water and dust, is a significant challenge for maintaining aerodynamic efficiency and maximizing energy output. Over time, this wear not only reduces blade lifespan but also leads to energy losses. To address this issue, we present a novel approach that combines drone-based high-resolution image capture with advanced 3D modeling techniques to predict and quantify erosion on turbine blade leading edges with a high accuracy. Our workflow involves capturing sharp, low-noise images despite the challenges posed by wind and blade motion. We employ techniques such as LiDAR-based localization, high dynamic range (HDR) imaging, and structure-from-motion (SfM) reconstruction to create precise 3D models of blade surfaces. Once we have an accurate 3D reconstruction, we perform defect detection and quantification to measure critical erosion metrics such as volume, surface area, and depth. Additionally, we address the computational challenge of processing 3D point clouds efficiently by leveraging depth maps, which represent the 3D model by a set of 2D images. This allows the fusion of visual and geometric information for improved defect detection. Compared to purely image-based detection, this fusion helps resolve visual ambiguities and allows the computation of physically meaningful defect metrics. Our method has demonstrated significant improvements in 3D reconstruction accuracy, with a reduction in reprojection error by more than 38% compared to state-of-the-art methods. The result is a denser, more reliable model of the turbine blades, which enables a more effective analysis of the surface defects. In turn, this will translate to more effective predictive and preventive maintenance strategies. Future work will focus on integrating joint deblurring and 3D reconstruction techniques, as well as incorporating prior knowledge to further refine defect quantification and prediction.