Research on wind turbine blade vibration detection method based on YOLOv5

In response to the problems of high detection cost and difficult layout in traditional wind turbine blade fatigue loading vibration detection methods, this paper designed a visual vibration detection method based on the YOLOv5s model. By training and tracking the feature points on wind turbine blades, the vibration displacement of the blades is obtained. A lightweight and parameter-free SimAM attention module is embedded in the residual module of the algorithm network to solve the problem of false detection and missed detection that may occur in complex environments. The experimental results show that the improved model can more accurately identify feature points on the blade. This method can effectively obtain the amplitude curve of the blade and adapt to complex on-site environments, making it more practical for vibration detection under blade fatigue loading.