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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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 C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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...
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
IR Spectrum01:19

IR Spectrum

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% (complete...
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in the 3500–3100 cm−1 range. Even though both O−H and N−H bonds vibrate at a similar...

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  3. Dmnet: A Frequency-enhanced And Adaptive Spatial Fusion Network For Rgb-infrared Object Detection.
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  3. Dmnet: A Frequency-enhanced And Adaptive Spatial Fusion Network For Rgb-infrared Object Detection.

Related Experiment Video

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

DMNet: A Frequency-Enhanced and Adaptive Spatial Fusion Network for RGB-Infrared Object Detection.

Yuchen Yao1,2, Xinlong Wang1,2, Yan Liu1,2

  • 1Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China.

Sensors (Basel, Switzerland)
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

DMNet, a novel dual-stream framework, enhances visible and infrared (IR) multimodal object detection by fusing complementary data. This efficient model excels in complex conditions, improving detection of small objects and in low light.

Keywords:
RGB–infrared fusionadaptive fusionfrequency-domain featuresmultimodal object detectionsmall object detection

Related Experiment Videos

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Object detection in challenging environments (illumination variations, clutter, small objects) is difficult.
  • Multimodal detection using RGB and infrared (IR) data offers complementary information but faces challenges like feature misalignment and detail loss.
  • Existing methods lack sufficient semantic interaction for robust multimodal object detection.

Purpose of the Study:

  • To introduce DMNet, a novel dual-stream framework for enhanced visible and IR multimodal object detection.
  • To address cross-modal feature misalignment, loss of fine-grained details, and insufficient semantic interaction in existing methods.
  • To develop an efficient and effective solution for object detection in complex, low-light, and small-object scenarios.

Main Methods:

  • Developed a dual-stream framework, DMNet, integrating Surface Detail Fusion (SDF), Wavelet Feature Extraction (WFE), Context-Guided Enhancement (CGE), and Adaptive Spatial Fusion (ASF).
  • SDF aligns shallow features, WFE enhances frequency-domain information, CGE refines semantics, and ASF aggregates multi-scale features.
  • Evaluated DMNet on M3FD, LLVIP, and VEDAI benchmark datasets.

Main Results:

  • DMNet achieved superior detection performance compared to existing methods across three benchmark datasets.
  • Achieved mAP@0.5 scores of 78.4% on M3FD, 94.4% on LLVIP, and 59.0% on VEDAI.
  • The model demonstrates high efficiency with only 5.72 million parameters, suitable for practical deployment.

Conclusions:

  • DMNet effectively overcomes challenges in visible and IR multimodal object detection, particularly in low-light and small-object scenarios.
  • The proposed framework offers a significant improvement in detection accuracy and efficiency.
  • DMNet presents a practical and high-performing solution for complex object detection tasks.