Improvement and Research on Object Detection Algorithm for Wind Turbine Blade Defect Scenarios Based on YOLOv8

Wind turbine blades are vital for energy generation, where defects can cause efficiency loss and costly maintenance. This paper proposes an improved object detection algorithm based on YOLOv8 for detecting defects in wind turbine blades. Enhancements include network architecture modifications and advanced attention mechanisms, which boost detection accuracy while maintaining real-time processing. Our approach is tested on a custom dataset, showing better performance than the standard YOLOv8 model. These improvements can enhance automated defect detection in wind turbines, reducing downtime and operational costs, and contributing to more efficient renewable energy maintenance.