Surface defect detection method for wind turbine blade based on improved YOLOv5

白家宇; 沙玲; 魏丹; 雷菊阳

doi:10.19666/j.rlfd.202412241

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Surface defect detection method for wind turbine blade based on improved YOLOv5

Thermal Power Generation Vol. 54, Issue 9, Pages: 95-103(2025)

更新时间：2026-08-11

- Surface defect detection method for wind turbine blade based on improved YOLOv5
- 作者机构：
  
  上海工程技术大学机械与汽车工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202412241
  CLC： TP391.41;TM315
- Published：2025
- 稿件说明：
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[1]白家宇,沙玲,魏丹,等.基于改进YOLOv5的风机叶片表面缺陷检测方法[J],2025,54(09):95-103.
[1]白家宇,沙玲,魏丹,等.基于改进YOLOv5的风机叶片表面缺陷检测方法[J],2025,54(09):95-103. DOI： 10.19666/j.rlfd.202412241.

DOI：10.19666/j.rlfd.202412241.

摘要

针对风机叶片表面缺陷复杂，传统卷积神经网络涉及阈值筛选和非极大值抑制过程而增加计算的复杂性且不利于模型部署等问题。提出一种结合RT-DETR(real-time-detection transformer)与YOLOv5算法的风机叶片的缺陷检测方法。首先基于RepVGG和FasterNet对YOLOv5的主干神经网络进行重新设计，降低模型计算复杂程度；考虑到检测任务中存在小尺寸目标，在颈部网络中的特征融合部位引入高效注意力机制(efficientchannel attention

ECA)，从而增强对输出特征的表达能力；最后采用RT-DETR中的Decoder重构原网络的检测头，减少非极大值抑制对模型的影响。实验结果表明：改进的YOLO-RT检测模型在自制风机叶片表面缺陷数据集上的平均检测精度为87.2%，准确率为92.7%，相比原YOLOv5模型分别提高4.4百分点和8.0百分点；检测速率达到118.3帧/s，优于其他检测模型。改进后的算法能有效提高检测精度和速率，更适合应用在风机叶片表面缺陷的检测任务。

Abstract

In light of the intricate nature of surface defects in wind turbine blades

conventional convolutional neural networks face problems such as threshold screening and non-maximum suppression processes

which increase computational complexity and are not conducive to model deployment. A novel defect detection model that integrates real-time-detection transformer(RT-DETR) with YOLOv5 algorithm is proposed. Firstly

the backbone network of YOLOv5 is redesigned based on RepVGG and FasterNet to reduce the computational complexity of the model. Recognizing the presence of small-sized targets within the detection tasks

an efficient channel attention(ECA) mechanism is integrated into the neck network's feature fusion component

thereby augmenting the expressiveness of the output features. Finally

the detection head of original network is reconstructed with the Decoder from RT-DETR

minimizing the effect of non-maximum suppression on the model's performance. The experimental results show that

the average detection accuracy and accuracy of YOLO-RT are 87.2% and 92.7%

respectively

on a self-constructed dataset of wind turbine blade surface defects

reflecting improvements of 4.4 and 8.0 percentage points over the original YOLOv5 model. The detection rate reaches 118.3 frames per second

surpassing that of alternative detection models. The enhancements introduced in this algorithm significantly improve both detection accuracy and speed

making it highly suitable for practical applications in detecting surface defects on wind turbine blades.

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references

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