摘要
风电叶片作为风力发电机组的关键部件,其表面缺陷的检测对于确保风电机组的稳定运行和延长使用寿命至关重要。传统的人工检测方法效率低、易受人为因素影响,提出了一种基于Cascade R-CNN的风电叶片表面缺陷检测方法。通过高分辨率无人机拍摄风电叶片表面图像,获取高质量的检测数据。采用Cascade R-CNN模型,该模型通过级联结构逐步提升检测精度,能够有效识别不同尺度的缺陷,如裂纹、划痕和腐蚀等常见损伤。为了提高模型的鲁棒性和泛化能力,采用了特征增强技术和多尺度特征融合方法,增强了模型对不同尺度和复杂特征的敏感度。通过数据扩充技术(如旋转、平移和缩放等变换)增加了训练样本的多样性,从而进一步提升了模型对不同光照、背景以及缺陷类型的适应能力。实验结果表明,该方法在平均精度(AP)上达到88.2%,在平均召回率(AR)上达到75.9%,显著优于传统检测方法,展示了更高的检测精度和效率。该方法不仅提升了风电叶片缺陷检测的精度和效率,也为风电叶片的智能化监控提供了有力的技术支持。
As a key component of wind turbines,the detection of surface defects on wind turbine blades is crucial for ensuring the stable operation and extending the service life of wind turbines.The traditional manual detection methods are inefficient and susceptible to human interference.This article proposed a surface defect detection method for wind turbine blades based on Cascade R-CNN.High-quality inspection data was obtained by capturing surface images of wind turbine blades using high-resolution drones.The Cascade R-CNN model was adopted,which gradually improves the detection accuracy through a cascade structure and could effectively identify defects of different scales,common damages such as cracks,scratches,and corrosion.In order to improve the robustness and generalization ability of the model,the adopts feature enhancement techniques and multi-scale feature fusion methods to enhance the sensitivity of the model to different scales and complex features.This article also increased the diversity of training samples through data augmentation techniques such as rotation,translation and scaling,thereby further enhancing the model's adaptability to different lighting,backgrounds,and defect types.The experimental results show that this method achieves an average precision(AP)of 88.2%and an average recall rate(AR)of 75.9%,significantly better than traditional detection methods,demonstrating higher detection accuracy and efficiency.This method not only improves the accuracy and efficiency of defect detection for wind turbine blades,but also provides strong technical support for intelligent monitoring of wind turbine blades.
作者
毛颖杰
勾越
MAO Yingjie;GOU Yue(Windey Energy Technology Group Co.,Ltd.,Hangzhou 310000,China)
出处
《微特电机》
2025年第5期85-88,共4页
Small & Special Electrical Machines
