基于改进YOLOv5s的风电叶片表面缺陷检测方法

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中图分类号：TP391.4DOI：10.3969/j.issn.1004-132X.2025.09.023

A Method for Detecting Surface Defects on Wind Turbine Blades Based on Improved YOLOv5s

WANG Jun GAO Guibing* School of Mechanical and Electrical Engineering，Hunan University of Science and Technology ， Xiangtan，Hunan，411100

Abstract：In order to improve the inteligent，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. Secondly，the CBAM was embedded into the backbone extraction network，which enhanced the model’s ability 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.Finall，an improved detection method was used to detect defects in the blades ofa 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.

Key Words： wind turbine blade;defect detection; asymptotic feature pyramid network（AFPN） ; convolutionalblockattentionmodule（CBAM）

0 引言

我国2023年累计并网风电装机4.4亿千瓦，风电装机规模持续扩大，预计到2050年装机容量将达到 2.4TW 。（剩余15238字）