Detection of Defects in Wind Turbin Blade Based on Cascaded Adaptive Hybrid Attention Network

Wind turbine blades are susceptible to various types of damage caused by environmental factors, external forces, and other influences. Achieving efficient and accurate detection of damage defects in wind turbine blades is essential to extend the service life of wind turbines. To this end, this paper proposes a cascaded adaptive hybrid attention network (CAHAN). Firstly, a high-resolution wind turbine blade damage defects image dataset is constructed using the dual aggregation transformation (DAT) model and the contrast enhancement algorithm. Secondly, a global contextual attention pyramid (GCAP) module is proposed to effectively integrate global and local feature information. Then, a cascaded adaptive hybrid attention (CAHA) module is developed to design a PANet feature fusion network, which can effectively filter out redundant global feature information. In addition, the dynamic head (DH) modules covering scale, space, and task awareness are applied to wind turbine blade defects detection, greatly enhancing the model’s capability to detect blade damage defects. Finally, the proposed method is tested using wind turbine blade defect images collected from open web downloads. The results show that the CAHAN model achieves an overall detection accuracy of 97.9% and boasts a fast detection speed of 149.25 frames per second (FPS), which has superior overall performance compared to other object detection models. This method is useful for defect detection of wind turbine blade damage.