WTB-Net:基于ShuffleNet V2的轻量级风机叶片表面缺陷识别算法

doi:10.19936/j.cnki.2096-8000.20230828.010

复合材料科学与工程 ›› 2023, Vol. 0 ›› Issue (8): 66-71.DOI: 10.19936/j.cnki.2096-8000.20230828.010

WTB-Net:基于ShuffleNet V2的轻量级风机叶片表面缺陷识别算法

张睿, 文传博^*

上海电机学院电气学院,上海 201306

收稿日期:2022-07-12 出版日期:2023-08-28 发布日期:2023-10-20
通讯作者: 文传博(1981—),男,博士,教授,主要从事人工智能、风电设备故障诊断等方面的研究,chuanbowen@163.com。
作者简介:张睿(1995—),男,硕士研究生,主要从事计算机视觉、风机叶片表面缺陷的图像分类与目标检测等方面的研究。
基金资助:
国家自然科学基金项目(61973209);上海市自然科学基金项目(20ZR1421200);上海市地方高校能力建设项目(22010501100)

WTB-Net: A lightweight wind turbine blade surface defect recognition algorithm based on ShuffleNet V2

ZHANG Rui, WEN Chuanbo^*

Department of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China

Received:2022-07-12 Online:2023-08-28 Published:2023-10-20

摘要/Abstract

摘要： 由于现有风机叶片(WTB)的缺陷检测技术尚未得到广泛应用,且传统检测方法的鲁棒性较差,本文提出了一种轻量级风机叶片表面缺陷识别算法WTB-Net。通过无人机拍摄华东某沿海风电场的2 569张风机叶片图像,通过筛选分类和数据扩充建立WTB表面缺陷数据集。以ShuffleNet V2核心骨干特征提取网络为基础,引入选择性卷积核注意力机制SKNet,自适应地调整感受野大小,增强有用特征,抑制无用特征。最后采用激活函数Leaky-ReLU,减少沉默神经元的出现,避免了在输入为负值时使用ReLU导致的神经元失活。实验结果表明,算法在WTB数据集上的准确度达到了98.12%,比ShuffleNet V2提高了6.53%,且模型参数量仅为1.4 M。

关键词: 深度学习, 图像分类, ShuffleNet V2, 风机叶片, 复合材料

Abstract: As the defect detection technology of wind turbine blades has not been widely used and the robustness of traditional detection methods is poor, this paper proposes a lightweight wind turbine blade surface defect identification algorithm WTB-Net. 2 569 images of wind turbine blades from a coastal wind farm in East China are captured by an unmanned aircraft, and the WTB surface defect dataset is established through screening and classification and data expansion. Based on ShuffleNet V2 core backbone feature extraction network, SKNet, a selective convolutional kernel attention mechanism, is introduced to adaptively adjust the perceptual field size, enhance useful features and suppress useless features. Finally, the activation function Leaky-ReLU is used to reduce the appearance of silent neurons and avoid the deactivation of neurons caused by using ReLU when the input is negative. The experimental results show that the algorithm achieves an accuracy of 98.12% on the WTB dataset, which is 6.53% better than ShuffleNet V2, and the number of model parameters is only 1.4 M.

Key words: deep learning, image classification, ShuffleNet V2, wind turbine blade, composites

中图分类号:

TB332

张睿, 文传博. WTB-Net:基于ShuffleNet V2的轻量级风机叶片表面缺陷识别算法[J]. 复合材料科学与工程, 2023, 0(8): 66-71.

ZHANG Rui, WEN Chuanbo. WTB-Net: A lightweight wind turbine blade surface defect recognition algorithm based on ShuffleNet V2[J]. COMPOSITES SCIENCE AND ENGINEERING, 2023, 0(8): 66-71.

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WTB-Net:基于ShuffleNet V2的轻量级风机叶片表面缺陷识别算法

WTB-Net: A lightweight wind turbine blade surface defect recognition algorithm based on ShuffleNet V2

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