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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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...
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Infrared (IR) Spectroscopy: Overview01:09

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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IR Spectrometers01:25

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There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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Imaging Biological Samples with Optical Microscopy01:18

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Super-resolution Fluorescence Microscopy01:37

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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IR Spectrum01:19

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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
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Related Experiment Video

Updated: Aug 7, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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YOLO-FR: A YOLOv5 Infrared Small Target Detection Algorithm Based on Feature Reassembly Sampling Method.

Xingang Mou1, Shuai Lei1, Xiao Zhou1

  • 1School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces YOLO-FR, an infrared dim-small target detection model that reduces feature loss during sampling. The enhanced model significantly improves detection accuracy for challenging infrared targets.

Keywords:
YOLOfeature reassemblyinfrared dim-small target detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Infrared Imaging

Background:

  • Dim-small target detection in infrared imagery is crucial but challenging due to feature loss during network sampling.
  • Existing models often struggle with preserving spatial and semantic information, leading to reduced detection accuracy.

Purpose of the Study:

  • To propose YOLO-FR, a novel YOLOv5-based model designed to mitigate feature loss in infrared dim-small target detection.
  • To enhance feature representation and fusion strategies for improved detection performance.

Main Methods:

  • Developed a feature reassembly sampling approach to scale feature maps without altering feature information content.
  • Introduced an STD Block to minimize feature loss during down-sampling by encoding spatial information into channels.
  • Utilized the CARAFE operator to upscale feature maps while preserving feature mapping characteristics.
  • Improved the neck network to fuse backbone-extracted features with semantic information for a small receptive field detection head.

Main Results:

  • The YOLO-FR model achieved a mean Average Precision (mAP50) of 97.4%, a 7.4% improvement over the original network.
  • Demonstrated superior performance compared to existing methods like J-MSF and YOLO-SASE.
  • Effectively reduced feature loss during the sampling process, enhancing detection capabilities.

Conclusions:

  • The proposed YOLO-FR model significantly enhances infrared dim-small target detection accuracy by addressing feature loss issues.
  • Feature reassembly sampling, STD Block, CARAFE operator, and improved neck network fusion are effective strategies for this task.
  • YOLO-FR represents a substantial advancement in the field of infrared object detection.