Surface material recognition through machine learning using time of flight LiDAR

Daniel Tafone, Luke McEvoy, Yong Meng Sua, Patrick Rehain, Yuping Huang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We explore an active illumination approach for remote and obscured material recognition, based on quantum parametric mode sorting and single-photon detection. By raster scanning a segment of material, we capture the relationships between each mirror position’s peak count and location. These features allow for a robust measurement of a material’s relative reflectance and surface texture. Through inputting these identifiers into machine learning algorithms, a high accuracy of 99% material recognition can be achieved, even maintaining up to 89.17% accuracy when materials are occluded by a lossy and multi-scattering obscurant of up to 15.2 round-trip optical depth.

Original languageEnglish
Pages (from-to)1813-1824
Number of pages12
JournalOSA Continuum
Volume2
Issue number8
DOIs
StatePublished - 15 Aug 2023

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