Enhancing wind turbine blade damage detection with YOLO-Wind
Abstract This study presents an enhanced YOLOv8n framework for wind turbine surface damage detection, achieving 83.9% mAP@0.5 on the DTU dataset—a 2.3% improvement over baseline models. The architecture replaces standard convolutions with depthwise separable convolutions (DWConv) to optimize computa...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-03639-8 |
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