Thermal and RGB Images Work Better Together in Wind Turbine Damage Detection

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dc.contributor.authorSvystun, Serhii
dc.contributor.authorMelnychenko, Oleksandr
dc.contributor.authorRadiuk, Pavlo
dc.contributor.authorSavenko, Oleg
dc.contributor.authorSachenko, Anatoliy
dc.contributor.authorLysyi, Andrii
dc.date.accessioned2024-12-11T13:43:22Z
dc.date.available2024-12-11T13:43:22Z
dc.date.issued2024-12-05
dc.descriptionThermal and RGB images work better together in wind turbine damage detection / S. Svystun et al. International Journal of Computing. Vol. 23, no. 4. P. 1–9. URL: https://doi.org/10.48550/arXiv.2412.04114
dc.description.abstractThe 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.
dc.identifier.citationThermal and RGB images work better together in wind turbine damage detection / S. Svystun et al. International Journal of Computing. Vol. 23, no. 4. P. 1–9. URL: https://doi.org/10.48550/arXiv.2412.04114
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/17269
dc.language.isoen
dc.publisherResearch Institute for Intelligent Computer Systems
dc.subjectunmanned aerial vehicle
dc.subjectimage composition
dc.subjectmultispectral images
dc.subjectgreen energy
dc.subjectdata quality management
dc.subjectweighted overlay
dc.titleThermal and RGB Images Work Better Together in Wind Turbine Damage Detection
dc.typeСтаття
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