Перегляд за Автор "Svystun, Serhii"

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  • Ескіз
    Документ
    Dynamic Trajectory Adaptation for Efficient UAV Inspections of Wind Energy Units
    (IEEE, Inc., 2024-11-26) Svystun, Serhii; Melnychenko, Oleksandr; Radiuk, Pavlo; Savenko, Oleg; Sachenko, Anatoliy; Lysyi, Andrii
    The research presents an automated method for determining the trajectory of an unmanned aerial vehicle (UAV) for wind turbine inspection. The proposed method enables efficient data collection from multiple wind installations using UAV optical sensors, considering the spatial positioning of blades and other components of the wind energy installation. It includes component segmentation of the wind energy unit (WEU), determination of the blade pitch angle, and generation of optimal flight trajectories, considering safe distances and optimal viewing angles. The results of computational experiments have demonstrated the advantage of the proposed method in monitoring WEU, achieving a 78% reduction in inspection time, a 17% decrease in total trajectory length, and a 6% increase in average blade surface coverage compared to traditional methods. Furthermore, the process minimizes the average deviation from the optimal trajectory by 68%, indicating its high accuracy and ability to compensate for external influences.
  • Ескіз
    Документ
    Precision Slicing for Enhanced Defect Detection in High-Resolution Wind Turbine Blade Imagery
    (CEUR-WS.org, 2024-07-29) Svystun, Serhii; Melnychenko, Oleksandr; Radiuk, Pavlo; Savenko, Oleg; Sachenko, Anatoliy
    The analysis of high-resolution aerial imagery captured by unmanned aerial vehicles (UAVs) presents significant analytical challenges, primarily due to the minuscule size of observable objects and the variability in object scale influenced by UAV altitude and positioning. These factors often lead to diminished data fidelity and complicate the detection of smaller objects, which are critical in applications such as infrastructure monitoring. Traditional image processing techniques, which typically segment images into smaller, randomly cropped sections before analysis, must sufficiently address these challenges. In this work, we propose a novel defect detection framework for identifying minor to medium-sized damages on wind turbine blades (WTBs), a critical component in renewable energy production. The proposed framework, termed 'slice-aided inference,' enhances the existing methodologies by incorporating both traditional patch division and a novel, more advanced technique known as slice-aided hyper-inference. These techniques are rigorously assessed with various advanced deep learning models, emphasizing their efficiency in identifying surface defects. The empirical testing conducted as part of this study demonstrates significant enhancements in detection capabilities, leveraging a dataset of high-resolution UAV images to highlight the practical applications and effectiveness of the proposed framework in real-world scenarios.
  • Ескіз
    Документ
    Thermal and RGB Images Work Better Together in Wind Turbine Damage Detection
    (Research Institute for Intelligent Computer Systems, 2024-12-05) Svystun, Serhii; Melnychenko, Oleksandr; Radiuk, Pavlo; Savenko, Oleg; Sachenko, Anatoliy; Lysyi, Andrii
    The inspection of wind turbine blades (WTBs) is crucial for ensuring their structural integrity and operational efficiency. Traditional inspection methods can be dangerous and inefficient, prompting the use of unmanned aerial vehicles (UAVs) that access hard-to-reach areas and capture high-resolution imagery. In this study, we address the challenge of enhancing defect detection on WTBs by integrating thermal and RGB images obtained from UAVs. We propose a multispectral image composition method that combines thermal and RGB imagery through spatial coordinate transformation, key point detection, binary descriptor creation, and weighted image overlay. Using a benchmark dataset of WTB images annotated for defects, we evaluated several state-of-the-art object detection models. Our results show that composite images significantly improve defect detection efficiency. Specifically, the YOLOv8 model’s accuracy increased from 91% to 95%, precision from 89% to 94%, recall from 85% to 92%, and F1-score from 87% to 93%. The number of false positives decreased from 6 to 3, and missed defects reduced from 5 to 2. These findings demonstrate that integrating thermal and RGB imagery enhances defect detection on WTBs, contributing to improved maintenance and reliability.