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

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Abstract

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%.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Author information

Author notes

  1. Peng Yan, Shaokai Zheng, Qinghan Du and Daolei Wang have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China

    Xiaoyan Yu, Peng Yan, Shaokai Zheng, Qinghan Du & Daolei Wang

Authors
  1. Xiaoyan Yu
  2. Peng Yan
  3. Shaokai Zheng
  4. Qinghan Du
  5. Daolei Wang

Contributions

Xiaoyan Yu: Conceptualization, Methodology, Validation, Writing– original draft, Writing– review & editing. Peng Yan: Data curation, Investigation, Software, Validation. Shaokai Zheng: Methodology, Software. Qinghan Du: Software, Data curation. Daolei Wang: Software, Writing–review & editing.

Corresponding author

Correspondence toDaolei Wang.

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The authors declare no conflict of interest. All authors disclosed no relevant relationships.

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