A wind turbine damage detection algorithm designed based on YOLOv8

Liu, Lizhao; Li, Pinrui; Wang, Dahan; Zhu, Shunzhi

doi:10.1016/j.asoc.2024.111364

Cited by 16 publications

(1 citation statement)

References 24 publications

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“…Yao et al [20] proposed a lightweight cascaded feature fusion neural network model based on YOLOX, which made lightweight improvements to the backbone feature extraction network of the RepVGG structure, thereby improving the model's inference speed. Liu et al [21] proposed a detection algorithm based on YOLOv8, which significantly improved detection accuracy by enhancing the feature extraction capability of the backbone network, achieving an mAP of 79.9%. Yu et al [22], by integrating the CBAM attention mechanism, employing weighted BiFPN (BiFPN), and improving the loss function, proposed an enhanced YOLOV8 model to improve the accuracy and robustness of wind turbine blade damage image detection.…”

Section: Introductionmentioning

confidence: 99%

WTBD-YOLOv8: An Improved Method for Wind Turbine Generator Defect Detection

Tong,

Fan,

Peng

et al. 2024

Sustainability

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Wind turbine blades are the core components responsible for efficient wind energy conversion and ensuring stability. To address challenges in wind turbine blade damage detection using image processing techniques such as complex image backgrounds, decreased detection performance due to high image resolution, prolonged inference time, and insufficient recognition accuracy, this study introduces an enhanced wind turbine blade damage detection model named WTDB-YOLOv8. Firstly, by incorporating the GhostCBS and DFSB-C2f modules, the aim is to reduce the number of model parameters while enhancing feature extraction capability. Secondly, by integrating the MHSA-C2f module, which incorporates a multi-head self-attention mechanism, the focus on global information is enabled, thereby mitigating irrelevant background interference and reducing the impact of complex backgrounds on damage detection. Lastly, adopting the Mini-BiFPN structure improves the retention of features for small target objects in shallow networks and reinforces the propagation of these features in deep networks, thereby enhancing the detection accuracy of small target damage and reducing false negative rates. Through training and testing on the Wind Turbine Blade Damage Dataset (WTBDD), the WTDB-YOLOv8 model achieves an average precision of 98.3%, representing a 2.2 percentage point improvement over the original YOLOv8 model. Particularly noteworthy is the increase in precision from 93.1% to 97.9% in small target damage detection. Moreover, the total parameter count of the model decreases from 3.22 million in YOLOv8 to 1.99 million, marking a reduction of 38.2%. Therefore, the WTDB-YOLOv8 model not only enhances the detection performance and efficiency of wind turbine blade damage but also significantly reduces the model parameter count, showcasing its practical advantages in engineering applications.

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Section: Introductionmentioning

confidence: 99%