Structural damage detection of wind turbine based on YOLO11

Conventional methods for visually examining wind turbines have numerous constraints and frequently fall short of guaranteeing optimal outcomes. Flaws or impairments on wind turbine blades diminish the lifespan and energy production efficiency of the turbine, while also escalating monitoring inaccuracies, safety hazards, and maintenance expenses. The automated assessment of wind turbines via deep learning has become a powerful and efficient method for tackling quality control challenges in the industrial sector, particularly through object detection methods using the YOLO versions that improves the detection of minor objects. Consequently, this research suggests identifying wind turbine faults through the latest version of YOLO (YOLO11) lightweight model that was released in 2024, which includes enhancements in YOLO’s series network effectiveness, precision, and real-time object detection features. YOLO11 incorporates an improved backbone and neck architecture, introducing components like the C3k2 (Cross Stage Partial with kernel size 2) block, SPPF (Spatial Pyramid Pooling - Fast), and C2PSA (Convolutional block with Parallel Spatial Attention), which enhance feature extraction and lead to higher precision, allowing the model to achieve better computational efficiency without compromising accuracy. Defect detection on wind turbines is challenging because there is a high similarity between the defects and the structure itself, but also the very small size of the defects on the structure may not be detected. Through the results obtained but also by comparing our findings with the studies already done, in this article we have demonstrated that YOLO11 is capable of detecting several defects at the same time with precision. In consequence our model can assist in improving wind turbines inspection.