基于端到端深度学习框架的绝对式光栅尺定位译码

Positioning and Decoding of Absolute Grating Rulers Based on End-to-End Deep Learning Framework

为了提高绝对式光栅尺定位译码精度,提出一种基于端到端深度学习框架的定位译码方法。融入注意力模块改进UNet++对码元边缘进行定位,设计码道边缘信息提取网络实现码道图像到位置信息的回归预测。为了减小累计误差,针对绝对光栅尺图像特点,设计一种损失函数将码元边缘定位网络与码道边缘信息提取网络整合为端到端网络框架,从而构建码道定位模块。设计基于码道中心像素的伪随机码解译方法,实现绝对光栅尺码道译码。实验结果表明,所提定位译码法能提升绝对式光栅尺测量精度,在95%置信区间内为(-0.206,0.243)μm,均方根误差为0.265μm,优于现有绝对式光栅尺定位译码方法。

To improve the accuracy of absolute grating ruler positioning and decoding,a positioning and decoding method based on end-to-end deep learning framework is proposed.Attention modules were integrated to improve the positioning of symbol edges of UNet++,and a channel edge information extraction network was designed to achieve regression prediction of channel images to position information.To reduce cumulative errors,a loss function was designed based on the characteristics of absolute grating ruler images,which integrates the symbol edge-positioning network and the code channel edge information extraction network into an end-to-end network framework,thereby constructing a code channelpositioning module.A pseudo-random code decoding method was designed based on the center pixel of the code path to achieve absolute grating size path decoding.The experimental results demonstrate that the proposed positioning decoding method can improve the measurement accuracy of absolute grating rulers,within a 95%confidence interval(-0.206,0.243)μm,the root mean square error is 0.265μm,superior to existing absolute grating ruler positioning and decoding methods.