Fibre-optic based particle sensing via deep learning. (30th September 2019)

• Record Type: Journal Article
• Authors: Grant-Jacob, James A; Jain, Saurabh; Xie, Yunhui; Mackay, Benita S; McDonnell, Michael D T; Praeger, Matthew; Loxham, Matthew; Richardson, David J; Eason, Robert W; Mills, Ben
• Is Part Of: JPhys photonics. Volume 1:Number 4(2019)
• Publication Date: 2019-09-30
• Subjects: deep learning -- fibres -- sensing -- optics -- microparticles -- particle pollution; Photonics -- Periodicals; 621.365
• DOI: 10.1088/2515-7647/ab437b
• Languages: English
• ISSNs: 2515-7647

Abstract

Abstract: We demonstrate the capability for the identification of single particles, via a neural network, directly from the backscattered light collected by a 30-core optical fibre, when particles are illuminated using a single mode fibre-coupled laser light source. The neural network was shown to be able to determine the specific species of pollen with ∼97% accuracy, along with the distance between the end of the 30-core sensing fibre and the particles, with an associated error of ±6 μ m. The ability to be able to classify particles directly from backscattered light using an optical fibre has potential in environments in which transmission imaging is neither possible nor suitable, such as sensing over opaque media, in the deep sea or outer space.