基于轻量级卷积网络的风力发电机表面缺陷检测研究

Research on surface defect detection of wind turbine based on lightweight convolutional network

风电机组的发电效率和使用寿命与其表面的完好度相关。本研究针对传统风力发电机表面缺陷检测方式出现的检测结果不够精准,检测用时较长的问题,设计了一种风力发电机表面缺陷检测模型。首先,在模型中融合了轻量级卷积技术,有效地增强了通道间信息交互的能力,通过更丰富的特征信息提高小尺寸缺陷的检测效果;其次,在主干网络引入了视觉注意力网络模块,丰富了上下文的信息,提升了卷积神经网络的特征提取的能力;然后,在颈部网络中引入协调注意力机制,通过空间方向捕捉缺陷的位置信息;最后,将边框回归的损失函数修改为WIoU,从而制定出合适的梯度增益分配策略。实验结果表明,改进后的检测模型相较于原模型检测精确度提高4.14%,显著提升了细小缺陷的检测能力;同时,改进后的模型的参数量降低了2.29 M,参数量则是降低了6.2 G,检测速度提升显著,满足模型实时检测的需求。

The power generation efficiency and service life of wind turbines are related to their surface integrity.This study aims to address the issue of inaccurate detection results and long detection time in traditional surface defect detection methods for wind turbines.A surface defect detection model for wind turbines is designed.Firstly,lightweight convolution technology is integrated into the model,effectively enhancing the ability of information exchange between channels and improving the detection effect of small-sized defects through richer feature information.Secondly,the visual attention network module has been introduced into the backbone network,enriching the contextual information and improving the feature extraction ability of convolutional neural networks.Then,a coordinated attention mechanism is introduced into the neck network to capture the location information of defects through spatial orientation.Finally,modify the loss function of bounding box regression to WIoU to develop an appropriate gradient gain allocation strategy.The experimental results show that the improved detection model improves the detection accuracy by 4.14%compared to the original model,significantly enhancing the detection ability of small defects.At the same time,the parameter count of the improved model was reduced by 2.29 M,while the parameter count was reduced by 6.2 G.The detection speed was significantly improved,meeting the real-time detection requirements of the model.