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Updated: Jun 22, 2026

High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging
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A Deep Learning Approach to Distance Map Generation Applied to Automatic Fiber Diameter Computation from Digital

Alain M Alejo Huarachi1, César A Beltrán Castañón1

  • 1Engineering Department, Pontificia Universidad Católica del Perú, Lima 15088, Peru.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
Summary
This summary is machine-generated.

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This study introduces a new deep learning method for precise fiber diameter measurement in textiles. The approach accurately segments individual fibers, improving quality control and pricing in the textile industry.

Area of Science:

  • Textile Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate fiber diameter measurement is vital for textile quality control and pricing.
  • Traditional methods and existing computer vision techniques face challenges with densely packed or overlapping fibers.

Purpose of the Study:

  • To develop a novel deep-learning-based method for automated fiber segmentation and diameter calculation.
  • To overcome limitations of current methods in analyzing complex fiber arrangements.

Main Methods:

  • A modified U-Net architecture was employed to automatically generate distance maps from fiber micrographs.
  • The model was trained using a combination of real and simulated micrograph data.

Main Results:

Keywords:
convolutional neural networkdeep learningdistance mapfiber micrographregressionsynthetic images

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  • The deep learning model achieved a mean absolute error (MAE) of 0.1094 and a mean square error (MSE) of 0.0711.
  • The method demonstrated effectiveness in separating individual fibers, even in challenging, dense, or overlapping scenarios.

Conclusions:

  • Deep learning offers a precise and automated solution for fiber analysis in the textile industry.
  • The developed method has the potential to significantly improve accuracy in fiber diameter measurement for quality assessment and pricing.