Open AccessDeep-learning-enhanced ice thickness measurement using Raman scattering
Author(s) -
Mingguang Shan,
Qi Cheng,
Zhao Zhi,
Bin Liu,
Yabin Zhang
Publication year - 2019
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.378735
Subject(s) - raman scattering , raman spectroscopy , optics , residual , materials science , remote sensing , data set , computer science , interface (matter) , geology , artificial intelligence , physics , algorithm , capillary number , capillary action , composite material
In ice thickness measurement (ICM) procedures based on Raman scattering, a key issue is the detection of ice-water interface using the slight difference between the Raman spectra of ice and water. To tackle this issue, we developed a new deep residual network (DRN) to cast this detection as an identification problem. Thus, the interface detection is converted to the prediction of the Raman spectra of ice and water. We enabled this process by designing a powerful DRN that was trained by a set of Raman spectral data, obtained in advance. In contrast to the state-of-the-art Gaussian fitting method (GFM), the proposed DRN enables ICM with a simple operation and low costs, as well as high accuracy and speed. Experimental results were collected to demonstrate the feasibility and effectiveness of the proposed DRN.