中国机械工程2025，Vol.36Issue(9)：2108-2117,10.DOI:10.3969/j.issn.1004-132X.2025.09.023
基于改进YOLOv5s的风电叶片表面缺陷检测方法
A Method for Detecting Surface Defects on Wind Turbine Blades Based on Improved YOLOv5s
摘要
Abstract
In order to improve the intelligent,efficient,and convenient development of wind turbine blade health monitoring technology,a wind turbine blade surface defect detection method was proposed based on improved YOLOv5s algorithm according to target recognition technology.Firstly,the original backbone network of YOLOv5s was replaced with an AFPN to enhance the network's learning ability.Sec-ondly,the CBAM was embedded into the backbone extraction network,which enhanced the model's abil-ity to extract surface defect features of leaves.Then,the minimum point distance intersection over union(MPDIoU)loss function was used to replace the CIoU loss function,improving the precision of bounding box localization.Finally,an improved detection method was used to detect defects in the blades of a certain wind turbine unit.The detection results show that the improved algorithm improves precision,recall and mean average precision(mAP)by 4.1%,2.9%and 4.8%,respectively,reaching as 91.9%,89.3%and 93.5%,which has significant precision advantages and better model stability.关键词
风电叶片/缺陷检测/渐进特征金字塔网络/卷积块注意力模块Key words
wind turbine blade/defect detection/asymptotic feature pyramid network(AFPN)/con-volutional block attention module(CBAM)分类
信息技术与安全科学引用本文复制引用
王俊,高贵兵..基于改进YOLOv5s的风电叶片表面缺陷检测方法[J].中国机械工程,2025,36(9):2108-2117,10.基金项目
湖南省自然科学基金(2023JJ60145) （2023JJ60145）
湖南省杰出青年基金(2024JJ2031) （2024JJ2031）
湖南省科技创新计划(2023RC3174) （2023RC3174）
