# A Deep Learning Approach to Distance Map Generation Applied to Automatic Fiber Diameter Computation from Digital Micrographs

## TL;DR

This paper presents a deep learning method to accurately measure fiber diameters in micrographs, improving textile quality assessment.

## Contribution

A novel deep-learning-based method using a modified U-Net for fiber diameter computation in micrographs.

## Key findings

- The model achieves a mean absolute error of 0.1094 in fiber diameter measurement.
- The approach effectively separates fibers in complex, densely packed micrographs.
- The method offers a precise and automated solution for textile quality control.

## Abstract

Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of 0.1094 and a mean square error (MSE) of 0.0711, demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing.