侧边抛磨光纤抛磨表面粗糙程度分析

Surface roughness analysis of side-polished fiber

为探索利用侧边抛磨光纤(SPF)实现微探针型的“光纤上的实验室”,利用灰度共生矩阵(GLCM)纹理特征分析方法分析SPF抛磨表面粗糙程度;基于基尼系数和袋外样本(OOB)误差估计的随机森林(RF)特征重要性排序方法优选纹理特征子集,实现在SPF抛磨表面上精确识别不同粗糙程度的抛磨区域。实验表明,SPF抛磨表面平坦区域具有对比度均值、熵均值高,二阶矩均值、均匀性均值以及相关性均值低的纹理特征特点;基于纹理特征重要性排序方法,衡量了GLCM纹理特征各参数对SPF抛磨表面不同粗糙程度分类识别敏感的强弱,优选出由方差、二阶矩、熵、对比度构成的特征子集,结果表明,利用优选特征子集对SPF抛磨表面粗糙度进行RF分类验证实验,分类精度可达到95.65%。可为探索SPF传感器件中粗糙抛磨表面与环境材料的光耦合机制对传感器灵敏度的影响提供依据,为探索在SPF抛磨面上精确识别高传感灵敏度区域提供参考。

To explore the use of side-polished fibre(SPF)for microprobe-type“lab-on-fibre”,this paper analyzes the surface roughness in side-polished fiber(SPF)by employing the gray level co-occurrence matrix(GLCM)texture feature analysis method.Our experimental results show the flat areas of the SPF polished surface exhibit texture characteristics with high mean values in contrast and entropy,and low mean values in the angular second moment(ASM),homogeneity,and correlation.By employing the random forest(RF)feature importance ranking method based on the Gini coefficient and out-of-bag(OOB)error estimation,our study assesses the sensitivity of various GLCM texture parameters in classifying different roughness levels of the SPF polished surfaces.A feature subset comprising variance,ASM,entropy,and contrast is identified as optimal.Through utilizing this subset,an RF classification validation experiment is conducted on the roughness of the SPF polished surfaces,with results showing an RF classification accuracy of 95.65%.Our research provides evidence for exploring the impact of rough polished surfaces in SPF optic sensors on the light coupling mechanism with environmental materials and its influence on sensor sensitivity.It lays the foundation for exploring the precise identification of high-sensitivity areas on SPF polished surfaces.