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Publikace detail

Thickness Estimation of Plastic Polymers in SWIR Imaging Using Deep Learning Methods

Autoři: Štursa Dominik | Rais Vítek

Rok: 2025

Druh publikace: ostatní - článek ve sborníku

Strana od-do: nestránkováno

Tituly:

Jazyk	Název	Abstrakt	Klíčová slova
eng	Thickness Estimation of Plastic Polymers in SWIR Imaging Using Deep Learning Methods	This work demonstrates accurate non-contact measurement of polymer thickness from a single short-wave infrared (1350 nm) image. We captured 1 211 ABS samples of ten nominal thicknesses (0.5 to 5.2 mm) cropped to 224 × 224 images for supervised regression. Four model families were benchmarked: a five region if interest mean values as features to feed FFNN, a full-image FFNN, a compact CNN, and five YOLOv11 backbones modified with a linear head. After 5 to 10 independent trainings per model, the tiny YOLOv11-n achieved the best median of mean square errors of 0,0046 (0.08 mm RMSE), outperforming CNN (0.015) and both dense baselines (greater than 0.07). The results confirm that SWIR imaging combined with modern detection backbones enables fast polymer metrology suitable for thickness estimation.	short-wave infrared (SWIR) imaging; polymer thickness; neural network regression