A Wind Turbine Blade Damage Detection Method

With the rapid development of wind power generation technology, the maintenance and management of wind turbine blades have become particularly important. Damage to wind turbine blades can cause a series of serious consequences, such as jeopardizing personnel safety, affecting economic benefits, and in severe cases, causing fuel leakage and environmental pollution. In order to quickly and accurately realize the automatic monitoring of wind turbine blade damage, this paper proposes a wind turbine blade damage identification method based on the improved YOLOv8 algorithm. Compared with other YOLO series algorithms, this algorithm not only improves the recognition accuracy, but also reduces the model size, improves the computational speed and saves the detection time. The algorithm can improve the efficiency and accuracy of wind turbine blade damage detection, reduce the maintenance cost, reduce the safety risk, economic risk, environmental risk, and improve the stability of wind farm operation.