Research on fast detection method of wind turbine in remote sensing image land area based on yolo

With the development of the social economy, wind turbines are taking up a larger and larger share of new energy sources. The detection of the number and spatial distribution of wind turbines in remotely sensed images holds great scientific significance. Wind turbines are difficult to identify in remote sensing images therefore, a fast detection method based on deep learning is proposed. First, we extract potential wind turbine candidate regions from wind speed, slope, and land use data. Second, the YOLO v5 model was trained using our labeled wind turbine detection dataset. Finally, the images of the candidate regions were used for wind turbine detection using the trained optimal model. The proposed method was demonstrated to have a recall of 94.87% and an accuracy of 82.04% through the experimental results. The proposed method for wind turbine detection is not only reasonable and effective but also offers a heightened level of efficiency.