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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

657
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
657

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Updated: May 14, 2025

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Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency Normalization.

Farshid Rayhan1, Jitesh Joshi1, Guangyu Ren1

  • 1Department of Computer Science, University College London, London NW1 2AE, UK.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces RGBT-Textile, a new dataset for close-range textile damage segmentation. A novel method, ThermoFreq, improves segmentation accuracy by reducing thermal noise, enhancing object identification in challenging scenes.

Keywords:
RGB-Thermal datasetsemantic segmentationtextile damage detection

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Area of Science:

  • Computer Vision
  • Materials Science
  • Image Processing

Background:

  • RGB-Thermal (RGBT) semantic segmentation is crucial for high dynamic range scene analysis.
  • Thermal imaging offers enhanced close-range feature extraction, vital for applications like textile damage detection.

Purpose of the Study:

  • Introduce RGBT-Textile, a specialized dataset for close-range textile and damage segmentation.
  • Present ThermoFreq, a novel thermal frequency normalization method to mitigate temperature noise in RGBT segmentation tasks.

Main Methods:

  • Developed a meticulous data collection protocol, software tools, and labeling process with textile scientists.
  • Created the RGBT-Textile dataset for close-range textile and damage segmentation.
  • Introduced and applied the ThermoFreq method for thermal noise reduction.

Main Results:

  • Evaluated RGBT-Textile and six other RGBT datasets using state-of-the-art (SOTA) models.
  • Demonstrated superior performance of SOTA models when utilizing the ThermoFreq method.
  • Confirmed ThermoFreq's effectiveness in addressing noise challenges across diverse environmental conditions.

Conclusions:

  • The RGBT-Textile dataset and ThermoFreq method significantly advance close-range RGBT semantic segmentation for textile applications.
  • ThermoFreq effectively reduces noise, improving segmentation accuracy in challenging thermal imaging scenarios.
  • Public release of the dataset aims to foster further research and collaboration in the field.