Precision Slicing for Enhanced Defect Detection in High-Resolution Wind Turbine Blade Imagery

Скорочена інформація про документ
dc.contributor.authorSvystun, Serhii
dc.contributor.authorMelnychenko, Oleksandr
dc.contributor.authorRadiuk, Pavlo
dc.contributor.authorSavenko, Oleg
dc.contributor.authorSachenko, Anatoliy
dc.date.accessioned2024-08-07T09:39:19Z
dc.date.available2024-08-07T09:39:19Z
dc.date.issued2024-07-29
dc.descriptionSvystun S., Melnychenko O., Radiuk P., Savenko O., Sachenko A. Precision slicing for enhanced defect detection in high-resolution wind turbine blade imagery. CEUR–WS, ISSN. 1613–0073. 2024. Vol. 3736. P. 1–18. (Scopus, Q4). URL: https://ceur-ws.org/Vol-3736/paper1.pdf
dc.description.abstractThe 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.
dc.identifier.citationPrecision slicing for enhanced defect detection in high-resolution wind turbine blade imagery / S. Svystun et al. 1st International Workshop on Intelligent & CyberPhysical Systems (ICyberPhyS-2024) : CEUR-Workshop Proceedings, Khmelnytskyi, Ukraine, 28 June 2024 / ed. by T. Hovorushchenko et al. Vol. 3736. CEUR-WS.org, Aachen, 2024. P. 1–18. URL: https://ceur-ws.org/Vol-3736/paper1.pdf
dc.identifier.issn1613–0073
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/16633
dc.language.isoen
dc.publisherCEUR-WS.org
dc.subjectaerial imagery
dc.subjectdrone imaging
dc.subjectdefect detection
dc.subjectWTBs
dc.subjectslice-aided inference
dc.subjecthyper-inference
dc.subjectdeep neural networks
dc.subjectimage segmentation
dc.subjecthigh-resolution imaging
dc.subjectobject detection
dc.titlePrecision Slicing for Enhanced Defect Detection in High-Resolution Wind Turbine Blade Imagery
dc.typeТези доповідей
Файли
Контейнер файлів
Зараз показуємо 1 - 1 з 1
Ескіз
Назва:
Radiuk_Precision-slicing.pdf
Розмір:
2.19 MB
Формат:
Adobe Portable Document Format
Завантажити
Ліцензійна угода
Зараз показуємо 1 - 1 з 1
Назва:
license.txt
Розмір:
4.26 KB
Формат:
Item-specific license agreed upon to submission
Опис:
Завантажити
Зібрання
Кафедра комп’ютерних наук