基于深度学习的无人机巡检风机叶片表面缺陷智能检测方法OA

To achieve efficient and accurate defect detection for wind turbine blades,this study develops a UAV-based intelligent inspection system and investigates its core algorithms,including multi-stage defect screening,representative frame selection,and deep learning-based defect recognition.First,based on image features such as structural integrity and texture patterns,an improved EfficientNetV2-S network is proposed for preliminary damage screening(accuracy 87.5%).Next,Gaussian Mixture Clustering is employed to select representative damaged frames from video streams,effectively reducing data redundancy.Then,a YOLO-v11 network integrated with Transformer modules and dynamic anchor strategies is developed for precise defect localization and classification.Additionally,a comprehensive dataset containing 15 000 annotated UAV images of various defects(e.g.,cracks,delamination)is established for algorithm validation.Experimental results demonstrate that the system achieves 88.4%recall and 71.5%precision in single-frame detection,while video-level analysis improves to 95.0%recall and 86.4%precision.The method outperforms existing approaches in detecting multiple defect types under complex environmental conditions.The proposed system meets the requirements for non-contact inspection,real-time processing,high accuracy,and strong anti-interference capability,providing an effective solution for intelligent wind turbine maintenance.