Research on Wind Turbine Blade Defect Detection Method Based on Shuffle-YOLOv5

With the development of wind power generation technology, the quality and life of wind turbine blades have an significant impact on power generation efficiency and safety. In order to detect blade surface damage as soon as possible and deal with it in time, in this paper, an improved Shuffle-YOLOv5 wind turbine blade defect detection method is proposed, which improves the accuracy and efficiency of blade defect detection. The detection rate is increased by 7%, and the precision rate is 91%. The experimental results show that the method has high performance in the detection task of wind turbine blade defects, it meets the requirements of safety and operation stability of wind power generation equipment.