YOLOv8-WTDD: multi-scale defect detection algorithm for wind turbines

In addressing the challenges of wind turbine defect detection, such as different defect scales in UAV aerial photography, interference from different lighting conditions, and small-sized target defects leading to low detection accuracy and inaccurate localization, a YOLOv8-WTBB model based on YOLOv8...

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Bibliographic Details
Published in:The Journal of supercomputing 2025, Vol.81 (1), Article 32
Main Authors: Yu, Xiaoyan, Yan, Peng, Zheng, Shaokai, Du, Qinghan, Wang, Daolei
Format: Article
Language:English
Subjects:
Aerial photography
Algorithms
Compilers
Computer Science
Defects
Interpreters
Localization
Object recognition
Processor Architectures
Programming Languages
Regression models
Target detection
Wind turbines
Citations: Items that this one cites
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Summary:In addressing the challenges of wind turbine defect detection, such as different defect scales in UAV aerial photography, interference from different lighting conditions, and small-sized target defects leading to low detection accuracy and inaccurate localization, a YOLOv8-WTBB model based on YOLOv8 is proposed. Firstly, the Diverse Branch Block is designed to enhance multi-scale feature fusion capabilities. Next, the Receptive-Field Attention Convolution is introduced to focus on the spatial features of the receptive field, increasing the distinction between target features and the surrounding environment. Finally, introducing the Minimum Point Distance Intersection over the Union bounding box regression loss function notably improves localization accuracy in object detection and accelerates model convergence. Experimental results demonstrate that the proposed algorithm significantly outperforms the baseline network, with a 4.3% improvement in mean average precision, achieving 89.1%, and a 7.4% increase in mean average recall, reaching 84.8%.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-06487-x