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IR Frequency Region: Fingerprint Region01:03

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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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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.
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YOLO-B:An infrared target detection algorithm based on bi-fusion and efficient decoupled.

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The new YOLO-B infrared target detection algorithm enhances feature extraction and fusion, improving accuracy and recall. This advanced model offers superior performance compared to existing YOLO versions for infrared target identification.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • Existing YOLOv5s algorithm struggles with infrared target detection due to incomplete feature extraction and detection errors.
  • Infrared target detection requires robust feature representation to overcome challenges like low resolution and thermal noise.

Purpose of the Study:

  • To develop an improved infrared target detection algorithm, YOLO-B, addressing limitations of YOLOv5s.
  • To enhance feature extraction, information fusion, and prediction accuracy for infrared targets.

Main Methods:

  • Proposed CSPPF structure to expand the receptive field of the feature extraction network.
  • Implemented Bifusion Neck for effective fusion of shallow and deep features.
  • Utilized an efficient decoupled head for prediction and WIoUv3 loss for bounding box regression.

Main Results:

  • Each proposed improvement individually demonstrated superior detection accuracy.
  • YOLO-B achieved a 1.9% increase in accuracy, 7.3% in recall, 3.8% in mAP@0.5, and 4.6% in mAP@0.5:0.95 over YOLOv5s.
  • YOLO-B outperformed YOLOv7 and YOLOv8s in parameter efficiency and detection accuracy.

Conclusions:

  • The YOLO-B algorithm significantly improves infrared target detection performance.
  • The integrated enhancements in feature extraction, fusion, and prediction contribute to the algorithm's effectiveness.
  • YOLO-B presents a promising advancement for infrared target detection applications.