SURFACE DEFECT DETECTION OF WIND TURBINE BLADES BASED ON STRUCTURED RE-PARAMETERIZATION AND RT-DETR

Bai Jiayu; Sha Ling; Lei Juyang; Wei Dan

doi:10.19912/j.0254-0096.tynxb.2024-2158

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 297-306. DOI: 10.19912/j.0254-0096.tynxb.2024-2158

SURFACE DEFECT DETECTION OF WIND TURBINE BLADES BASED ON STRUCTURED RE-PARAMETERIZATION AND RT-DETR

Bai Jiayu, Sha Ling, Lei Juyang, Wei Dan

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Abstract

In view of the different shapes of surface defects of wind turbine blades,the traditional convolutional neural network involves threshold screening and non-maximum suppression processes that increase the complexity of calculation,a detection model RH-DETR based on improved Transformer （real-time-detection transformer,RT-DETR） is proposed by comparing backbone networks such as ResNet18,SwinTransformer and CSwinTransformer,ResNet18 with relatively balanced detection accuracy and low computational complexity is selected as the backbone feature extraction network; the improved re-parameter module （RP-Block） is introduced to update the basic-block of the backbone network,while not losing detection accuracy,improving the inference speed of the model; considering the irregular shape of surface defects on wind turbine blades, the HiLo structure is used to reconstruct the original AIFI structure to solve the problem of low-level feature loss and improve the feature extraction ability of the model. The experimental results show that the accuracy and average accuracy of RH-DETR are 96.1% and 93.1% respectively, which are better than the current mainstream YOLO detection model. Compared with the original model RT-DETR,they are improved by 2.4 and 1.3 percentage points, respectively. In addition, the computational complexity of the model is reduced by over 50%. The detection speed is increased from 32.6 f/s to 64.7 f/s,which meets the requirements of industrial real-time detection and provides technical support for the detection and maintenance of wind blade.

Key words

wind turbine blades / surface defects / deep learning / RT-DETR / attention mechanism / lightweight network

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Bai Jiayu, Sha Ling, Lei Juyang, Wei Dan. SURFACE DEFECT DETECTION OF WIND TURBINE BLADES BASED ON STRUCTURED RE-PARAMETERIZATION AND RT-DETR[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 297-306 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2158

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