Wind Turbine Blade Surface Defect Detection Based on YOLO Algorithm

For wind turbine operation and maintenance, wind turbine blade surface defect detection is a very important and challenging problem, as wind turbine blade surface defects seriously affect the efficiency and safety of the wind turbine. The performance of traditional methods depends heavily on the correlation between the handcrafted features and the features of the defects themselves, but surface defects are diverse which could make the traditional methods fail in reality. In this work, we present an automated framework to identify surface defects of blades using the advanced YOLOv5 algorithm, which can learn and extract blade surface defect features adaptively and accurately identify even very minor faults. The results of different algorithms are collected and compared based on a self-built dataset, which show that YOLOv5 has the best performance. In addition, YOLOv5 has significant advantages in terms of model size and training speed, and these advantages make the YOLOv5 model well suited for wind turbine blade surface defect recognition.