液晶与显示2026，Vol.41Issue(2)：267-279,13.DOI:10.37188/CJLCD.2026-0002
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
Intelligent detection method for surface defects on wind turbine blades based on deep learning UAV inspection
摘要
Abstract
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.关键词
风机叶片表面缺陷/无人机巡检/高斯混合聚类模型/YOLO-v11/EfficientNetV2Key words
surface defects of wind turbine blades/UAV tour-inspection/Gaussian mixture model/YOLO-v11/efficientNetV2分类
信息技术与安全科学引用本文复制引用
郑欣,田琳,张艳,甘宗源,黄威..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[J].液晶与显示,2026,41(2):267-279,13.基金项目
江西省教育厅科学技术研究项目(No.GJJ2402304) （No.GJJ2402304）
智能医学与健康大数据四川省高校重点实验室开放基金(No.ZNYX2501,No.ZNYX2514) （No.ZNYX2501,No.ZNYX2514）
政务数据安全达州市重点实验室开放基金(No.ZSAQ202514) （No.ZSAQ202514）
四川革命老区发展研究中心开放基金(No.SLQ2025SA04)Supported by Jiangxi Provincial Department of Education Science and Technology Research Project(No.GJJ2402304) （No.SLQ2025SA04）
Open Fund of Sichuan Provincial University Key Laboratory of Intelligent Medicine and Health Big Data(No.ZNYX2501,No.ZNYX2514) （No.ZNYX2501,No.ZNYX2514）
Open Fund of Dazhou Key Laboratory of Government Data Security(No.ZSAQ202514) （No.ZSAQ202514）
Open Fund of Sichuan Revolutionary Old Base Development Research Center(No.SLQ2025SA04) （No.SLQ2025SA04）
