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基于大视野模型转移学习的风力轮机叶片缺陷识别方法

Xin Li¹, Jinghe Tian¹, Xinfu Pang¹

¹Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China.

Sensors (Basel, Switzerland)

|July 30, 2025

概括

这项研究引入了用于检测风力轮机叶片缺陷的先进框架,达到97.8%的准确性. 新方法通过更快,更可靠的缺陷识别,提高风电场的安全性和维护效率.

科学领域:

可再生能源工程可再生能源工程
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

风力轮机叶片表面缺陷对运行安全和维护效率构成风险.
现有的缺陷检测方法在一般化,背景噪音和实时性能方面扎.

研究的目的:

开发一个端到端的框架,用于准确和高效的风力轮机叶片缺陷识别.
克服当前方法在概括,背景干扰和实时处理方面的局限性.

主要方法:

一个三阶段的框架:叶片定位 (YOLOv5),特征提取 (DINOv2) 和缺陷分类 (随机配置网络 - SCN).
利用DINOv2进行强大的特征表示,在复杂的纹理上优于传统的卷积神经网络 (CNN) 方法.

主要成果:

在缺陷检测方面实现了97.8%的分类准确度.
每张图像的平均推断时间为19.65毫秒,满足实时要求.

结论:

拟议的框架为智能风力轮机叶片检查和维护提供了可扩展,准确和高效的解决方案.
整合DINOv2显著提高了缺陷识别能力,特别是在复杂的表面.

关键词:

这就是DINOv2的意义.随机配置网络网络随机配置网络在 YOLOv5 网络中,YOLOv5 网络的检测缺陷检测检测缺陷检测的方法风叶片风叶片风叶片

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