Dual-channel model based on multi-attention mechanism for wind turbine blade surface target recognition

doi:10.19936/j.cnki.2096-8000.20250228.016

Abstract

Abstract: At present, the identification of defects in wind turbine blades mainly relies on two methods: telescopes and shutdown gondolas, but these methods have problems in accuracy and safety. With the widespread application of drone technology, the use of drones to identify surface defects on wind turbine blades has become a high-profile option. In this paper, a medium scale network combining Swin Transformer and lightweight neural network was proposed to identify the surface target of wind turbine blade using computer vision method. A dataset comprising 1 275 blade images from a coastal wind farm in East China was collected, and data augmentation techniques were employed to expand the dataset by a factor of five. In the model, the Swin Transformer served as the primary feature extractor, while the lightweight neural network functioned as the auxiliary feature extractor. The CBAM attention mechanism was introduced to enhance the model’s focus on crucial local information, and the learning rate was adjusted using the CosineAnnealingWarmRestarts strategy to optimize model performance. Experimental results show that the accuracy and F1-score of the model proposed in this paper reached 97.8% and 96.35% respectively, which are both ahead of the mainstream models of the same magnitude, providing a new method for wind turbine blade surface target recognition.

Key words: wind turbine blades, composites, image identification, Swin Transformer, CNN, attention mechanism

CLC Number:

TB332

KE Canyang, WEN Chuanbo. Dual-channel model based on multi-attention mechanism for wind turbine blade surface target recognition[J]. COMPOSITES SCIENCE AND ENGINEERING, 2025, 0(2): 129-136.

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