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Surface damage detection and identification method of wind turbine blades based on improved YOLOv8
Published:2024-08-02 author:WU Boyang, MAO Shengke, LIN Teyu, et al. Browse: 780 Check PDF documents

Surface damage detection and identification method of wind 
turbine blades based on improved YOLOv8


WU Boyang, MAO Shengke, LIN Teyu, REN Haojie, CAI Haiyang, LI Yang

(Windey Energy Technology Group Co., Ltd., Hangzhou 310012, China)


Abstract:  Aiming at the problem of the high manufacturing and maintenance costs of wind turbine blades, which were prone to damage and failure, a method for detecting and recognizing surface damages on wind turbine blades based on the improved YOLOv8 model was proposed. Firstly, the highdefinition blade images captured in the field were used as the dataset, which was randomly divided into a training set, validation set, and test set according to the proportion. Then, the dynamic data enhancement algorithms Mosaic and Mix up were introduced, as well as the offline data enhancement algorithm Albumentations, to expand the experimental training dataset and address the generalization problem of the model under a limited dataset. Finally, the convolutional block attention module (CBAM) and gradient harmonizing mechanism (GHM)/Focal loss algorithms were employed to strengthen the models ability to detect damage and improve the problem of sample distribution imbalance. These approaches aimed to establish an advanced wind turbine blade surface damage detection algorithm and enhance the accuracy of damage detection with theYOLOv8 model. The research results show that the improved YOLOv8 model outperforms the faster region convolutional neural network (Faster RCNN) model with the same configuration in terms of average precision (AP) and average recall (AR) with lower computational and parameter counts. The improved model achieves an AP and AR of 73.2% and 58.8% respectively at an Intersection over Union (IoU) threshold of 0.5, verifying the reliability and effectiveness of this method in wind turbine blade damage detection direction.

Key words:  wind turbine blade damage identification; YOLOv8; target detection; data augmentation algorithm; convolutional block attention module (CBAM); gradient harmonizing mechanism (GHM); average precision (AP); average recall (AR); faster region convolutional neural network (Faster RCNN); intersection over union (IoU)
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