Integrated hardware–software complex for wind turbine blade defect classification

Oleg Zastavnyy, Lesia Dubchak, Volodymyr Kochan, Oleg Savenko, Zenovii Bernas, Nadiia Vasylkiv

Abstract


The subject matter of this article is an integrated hardware–software complex for real-time detection and classification of wind turbine blade defects using an unmanned aerial vehicle (UAV) equipped with onboard edge-AI modules. The goal of the study is to develop and experimentally validate an autonomous embedded framework capable of collecting, processing, and interpreting blade-surface image data under field conditions while maintaining sufficient diagnostic accuracy and real-time performance on resource-constrained hardware. The tasks of the study are to design the architecture of the UAV-based inspection system; to implement an onboard processing pipeline including image acquisition, preprocessing, defect detection, classification, and georeferenced data association; to develop a lightweight YOLO-based visual detection stage combined with a neuro-fuzzy decision module; and to evaluate the diagnostic and computational performance of the proposed system on Raspberry Pi 5. The methods used include lightweight deep-learning-based visual analysis, neuro-fuzzy inference based on Gaussian membership functions and Wang–Mendel-type adaptation, telemetry-assisted defect georeferencing, and experimental evaluation under embedded deployment constraints. The results obtained show that the developed prototype provides stable onboard operation at 5–6 FPS for 640×480 video, supports local processing without cloud infrastructure, and ensures reliable defect-state recognition for four predefined blade-surface classes: Erosion, Corrosion, Crack, and Pristine. In the integrated validation workflow, the system achieved 88.1% classification accuracy, while the neuro-fuzzy decision module evaluated separately on YOLO-derived class-coefficient vectors achieved 94.0% accuracy with an MSE of 0.0686. Conclusions. The scientific novelty lies in the development of a fully autonomous UAV-based diagnostic architecture that combines onboard visual detection, telemetry fusion, and neuro-fuzzy decision support on limited embedded hardware, as well as in the adaptation of the neuro-fuzzy classifier for real-time multiclass blade-condition assessment

Keywords


wind turbine blades; UAV inspection; defect detection; deep learning; neuro-fuzzy system; edge-AI; YOLO; Raspberry Pi

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References


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DOI: https://doi.org/10.32620/reks.2026.1.08

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