摘要
基于层析技术的三维燃烧诊断需要利用多个方向的火焰图像进行三维重建,镜头角度标定的准确性直接影响重建质量和精度,为降低角度标定误差,提出基于十字标定块的卷积神经网络(Convolutional Neural Network,CNN)镜头角度标定方法。设计新型十字标定块,与传统标定块相比,其具有更复杂的空间结构特征,能够提升图像在旋转过程中的几何信息差异,有助于CNN更准确地提取角度特征,降低训练集中的角度标签误差。搭建基于残差神经网络(Residual Neural Network,ResNet)架构的CNN进行角度预测,并基于开源框架PyTorch实现CNN训练,避免了人工特征设计。开展实验对基于十字标定块的CNN镜头角度标定方法的应用效果进行验证,结果表明:采用传统三棱柱标定块进行镜头角度标定时,角度标签误差较大,导致镜头角度标定准确性较低;采用十字标定块进行镜头角度标定时,模型训练过程中损失函数收敛更快,镜头角度标定准确性更高。基于十字标定块的卷积神经网络镜头角度标定方法展现出更高的鲁棒性与更强的泛化能力,为提升燃烧图像重建准确性提供了技术支撑。
Three-dimensional combustion diagnosis based on tomography technology requires the use of flame images in multiple directions for three-dimensional reconstruction.The accuracy of lens angle calibration directly affects the quality and precision of reconstruction.In order to reduce the error of angle calibration,a Convolutional Neural Network(CNN)lens angle calibration method based on cross calibration block is proposed.A new cross calibration block was designed.Compared with the traditional calibration block,it has more complex spatial structure characteristics,which can enhance the geometric information difference of the image during rotation,help CNN to extract angle features more accurately,and reduce the angle label error in the training set.A CNN based on the Residual Neural Network(ResNet)architecture was built for angle prediction,and CNN training was implemented based on the open-source framework PyTorch to avoid artificial feature design.Experiments were conducted to verify the application effect of the CNN lens angle calibration method based on the cross calibration block.The results show that when the traditional triangular prism calibration block is used for lens angle calibration,the angle label error is large,resulting in low accuracy of lens angle calibration;when the cross calibration block is used for lens angle calibration,the loss function converges faster during model training and the accuracy of lens angle calibration is higher.The convolutional neural network lens angle calibration method based on the cross calibration block shows higher robustness and stronger generalization ability,providing technical support for improving the accuracy of combustion image reconstruction.
作者
唐梓潇
王若彤
雷庆春
TANG Zixiao;WANG Ruotong;LEI Qingchun(School of Electric and electronic engineering,University of Nottingham Ningbo China,Ningbo 315100,China;School of Aerospace engineering,University of Nottingham Ningbo China,Ningbo 315100,China;School of Power and Energy,Northwestern Polytechnical University,Xi'an 710129,China)
出处
《计测技术》
2025年第4期158-166,共9页
Metrology & Measurement Technology
基金
国家财政稳定支持项目(GJCZ-0202-04)。
关键词
三维测量
十字标定块
镜头角度
标定误差
卷积神经网络
3D measurement
cross calibration block
lens angle
calibration error
convolutional neural network
