TY - JOUR
T1 - Surface material recognition through machine learning using time of flight LiDAR
AU - Tafone, Daniel
AU - McEvoy, Luke
AU - Sua, Yong Meng
AU - Rehain, Patrick
AU - Huang, Yuping
N1 - Publisher Copyright:
© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
PY - 2023/8/15
Y1 - 2023/8/15
N2 - 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.
AB - 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.
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U2 - 10.1364/OPTCON.492258
DO - 10.1364/OPTCON.492258
M3 - Article
AN - SCOPUS:85170648111
VL - 2
SP - 1813
EP - 1824
JO - OSA Continuum
JF - OSA Continuum
IS - 8
ER -