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Smart Structures and Systems Volume 34, Number 2, August 2024 , pages 129-143 DOI: https://doi.org/10.12989/sss.2024.34.2.129 |
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Generation of wind turbine blade surface defect dataset based on StyleGAN3 and PBGMs |
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W.R. Li, W.H. Zhao, T.T. Wang and Y.F. Du
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| Abstract | ||
| In recent years, with the vigorous development of visual algorithms, a large amount of research has been conducted on blade surface defect detection methods represented by deep learning. Detection methods based on deep learning models must rely on a large and rich dataset. However, the geographical location and working environment of wind turbines makes it difficult to effectively capture images of blade surface defects, which inevitably hinders visual detection. In response to the challenge of collecting a dataset for surface defects that are difficult to obtain, a multi-class blade surface defect generation method based on the StyleGAN3 (Style Generative Adversarial Networks 3) deep learning model and PBGMs (Physics-Based Graphics Models) method has been proposed. Firstly, a small number of real blade surface defect datasets are trained using the adversarial neural network of the StyleGAN3 deep learning model to generate a large number of high-resolution blade surface defect images. Secondly, the generated images are processed through Matting and Resize operations to create defect foreground images. The blade background images produced using PBGM technology are randomly fused, resulting in a diverse and high-resolution blade surface defect dataset with multiple types of backgrounds. Finally, experimental validation has proven that the adoption of this method can generate images with defect characteristics and high resolution, achieving a proportion of over 98.5%. Additionally, utilizing the EISeg annotation method significantly reduces the annotation time to just 1/7 of the time required for traditional methods. These generated images and annotated data of blade surface defects provide robust support for the detection of blade surface defects. | ||
| Key Words | ||
| blade surface defects; computer vision; deep learning; PBGMs; structural health monitoring; StyleGAN3 | ||
| Address | ||
| (1) W.R. Li, W.H. Zhao, T.T. Wang, Y.F. Du: Institution of Earthquake Protection and Disaster Mitigation, Lanzhou University of Technology, Lanzhou 730050, China; (2) W.R. Li, Y.F. Du: International Research Base on Seismic Mitigation and Isolation of GANSU Province, Lanzhou University of Technology, Lanzhou 730050, China; (3) W.R. Li, Y.F. Du: Disaster Prevention and Mitigation Engineering Research Center of Western Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China. | ||