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WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX

Author

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  • Yuan Yao

    (School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China)

  • Guozhong Wang

    (School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China)

  • Jinhui Fan

    (School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
    Shanghai Media Group, Shanghai 200041, China)

Abstract

Wind turbine blades will suffer various surface damages due to their operating environment and high-speed rotation. Accurate identification in the early stage of damage formation is crucial. The damage detection of wind turbine blades is a primarily manual operation, which has problems such as high cost, low efficiency, intense subjectivity, and high risk. The rise of deep learning provides a new method for detecting wind turbine blade damage. However, in detecting wind turbine blade damage in general network models, there will be an insufficient fusion of multiscale small target features. This paper proposes a lightweight cascaded feature fusion neural network model based on YOLOX. Firstly, the lightweight area of the backbone feature extraction network concerning the RepVGG network structure is enhanced, improving the model’s inference speed. Second, a cascaded feature fusion module is designed to cascade and interactively fuse multilevel features to enhance the small target area features and the model’s feature perception capabilities for multiscale target damage. The focal loss is introduced in the post-processing stage to enhance the network’s ability to learn complex positive sample damages. The detection accuracy of the improved algorithm is increased by 2.95%, the mAP can reach 94.29% in the self-made dataset, and the recall rate and detection speed are slightly improved. The experimental results show that the algorithm can autonomously learn the blade damage features from the wind turbine blade images collected in the actual scene, achieve the automatic detection, location, and classification of wind turbine blade damage, and promote the detection of wind turbine blade damage towards automation, rapidity, and low-cost development.

Suggested Citation

  • Yuan Yao & Guozhong Wang & Jinhui Fan, 2023. "WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX," Energies, MDPI, vol. 16(9), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3776-:d:1135416
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    References listed on IDEAS

    as
    1. Wenjie Wang & Yu Xue & Chengkuan He & Yongnian Zhao, 2022. "Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades," Energies, MDPI, vol. 15(15), pages 1-31, August.
    2. Shyuan Cheng & Mahmoud Elgendi & Fanghan Lu & Leonardo P. Chamorro, 2021. "On the Wind Turbine Wake and Forest Terrain Interaction," Energies, MDPI, vol. 14(21), pages 1-13, November.
    3. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
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    Cited by:

    1. Mingwei Lei & Xingfen Wang & Meihua Wang & Yitao Cheng, 2024. "Improved YOLOv5 Based on Multi-Strategy Integration for Multi-Category Wind Turbine Surface Defect Detection," Energies, MDPI, vol. 17(8), pages 1-25, April.

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