YOLO Ensemble for UAV-based Multispectral Defect Detection in Wind Turbine Components
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918293175009280 |
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| author | Svystun, Serhii Radiuk, Pavlo Melnychenko, Oleksandr Savenko, Oleg Sachenko, Anatoliy |
| author_facet | Svystun, Serhii Radiuk, Pavlo Melnychenko, Oleksandr Savenko, Oleg Sachenko, Anatoliy |
| contents | Unmanned aerial vehicles (UAVs) equipped with advanced sensors have opened up new opportunities for monitoring wind power plants, including blades, towers, and other critical components. However, reliable defect detection requires high-resolution data and efficient methods to process multispectral imagery. In this research, we aim to enhance defect detection accuracy through the development of an ensemble of YOLO-based deep learning models that integrate both visible and thermal channels. We propose an ensemble approach that integrates a general-purpose YOLOv8 model with a specialized thermal model, using a sophisticated bounding box fusion algorithm to combine their predictions. Our experiments show this approach achieves a mean Average Precision (mAP@.5) of 0.93 and an F1-score of 0.90, outperforming a standalone YOLOv8 model, which scored an mAP@.5 of 0.91. These findings demonstrate that combining multiple YOLO architectures with fused multispectral data provides a more reliable solution, improving the detection of both visual and thermal defects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04156 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | YOLO Ensemble for UAV-based Multispectral Defect Detection in Wind Turbine Components Svystun, Serhii Radiuk, Pavlo Melnychenko, Oleksandr Savenko, Oleg Sachenko, Anatoliy Computer Vision and Pattern Recognition Artificial Intelligence Robotics 68T07, 68T45, 68U10, 68T40 I.2.10; I.4.8; I.5.4; I.2.9 Unmanned aerial vehicles (UAVs) equipped with advanced sensors have opened up new opportunities for monitoring wind power plants, including blades, towers, and other critical components. However, reliable defect detection requires high-resolution data and efficient methods to process multispectral imagery. In this research, we aim to enhance defect detection accuracy through the development of an ensemble of YOLO-based deep learning models that integrate both visible and thermal channels. We propose an ensemble approach that integrates a general-purpose YOLOv8 model with a specialized thermal model, using a sophisticated bounding box fusion algorithm to combine their predictions. Our experiments show this approach achieves a mean Average Precision (mAP@.5) of 0.93 and an F1-score of 0.90, outperforming a standalone YOLOv8 model, which scored an mAP@.5 of 0.91. These findings demonstrate that combining multiple YOLO architectures with fused multispectral data provides a more reliable solution, improving the detection of both visual and thermal defects. |
| title | YOLO Ensemble for UAV-based Multispectral Defect Detection in Wind Turbine Components |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics 68T07, 68T45, 68U10, 68T40 I.2.10; I.4.8; I.5.4; I.2.9 |
| url | https://arxiv.org/abs/2509.04156 |