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Surface defect detection method for wind turbine blade based on improved YOLOv5
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Jiayu BAI, Ling SHA, Dan WEI, Juyang LEI
Thermal Power Generation | 2025, 54(9) : 95 - 103
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Thermal Power Generation | 2025, 54(9): 95-103
Special topic on low carbon power technology
Surface defect detection method for wind turbine blade based on improved YOLOv5
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Jiayu BAI, Ling SHA, Dan WEI, Juyang LEI
Affiliations
  • School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
Published: 2025-09-25 doi: 10.19666/j.rlfd.202412241
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In light of the intricate nature of surface defects in wind turbine blades, conventional convolutional neural networks face problems such as threshold screening and non-maximum suppression processes, which increase computational complexity and are not conducive to model deployment. A novel defect detection model that integrates real-time-detection transformer (RT-DETR) with YOLOv5 algorithm is proposed. Firstly, the backbone network of YOLOv5 is redesigned based on RepVGG and FasterNet to reduce the computational complexity of the model. Recognizing the presence of small-sized targets within the detection tasks, an efficient channel attention (ECA) mechanism is integrated into the neck network’s feature fusion component, thereby augmenting the expressiveness of the output features. Finally, the detection head of original network is reconstructed with the Decoder from RT-DETR, minimizing the effect of non-maximum suppression on the model’s performance. The experimental results show that, the average detection accuracy and accuracy of YOLO-RT are 87.2% and 92.7%, respectively, on a self-constructed dataset of wind turbine blade surface defects, reflecting improvements of 4.4 and 8.0 percentage points over the original YOLOv5 model. The detection rate reaches 118.3 frames per second, surpassing that of alternative detection models. The enhancements introduced in this algorithm significantly improve both detection accuracy and speed, making it highly suitable for practical applications in detecting surface defects on wind turbine blades.

defect detection  /  wind turbine blade  /  YOLOv5  /  attention mechanism  /  RT-DETR
Jiayu BAI, Ling SHA, Dan WEI, Juyang LEI. Surface defect detection method for wind turbine blade based on improved YOLOv5[J]. Thermal Power Generation, 2025 , 54 (9) : 95 -103 . DOI: 10.19666/j.rlfd.202412241
  • National Natural Science Foundation of China(62101314)
Year 2025 volume 54 Issue 9
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doi: 10.19666/j.rlfd.202412241
  • Receive Date:2024-12-05
  • Online Date:2026-03-05
  • Published:2025-09-25
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  • Received:2024-12-05
Funding
National Natural Science Foundation of China(62101314)
Affiliations
    School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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