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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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IDDNet: Infrared Object Detection Network Based on Multi-Scale Fusion Dehazing.

Shizun Sun1, Shuo Han1, Junwei Xu1

  • 1School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China.

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Summary

This study introduces IDDNet, a lightweight network for infrared object detection in fog. IDDNet enhances image clarity and object visibility, improving detection accuracy and real-time performance in adverse weather conditions.

Keywords:
attention mechanismdeep learningdehazingfeature fusioninfrared object detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Foggy conditions degrade infrared image quality, reducing contrast and detail.
  • Impaired image quality negatively impacts object detection accuracy and real-time processing.

Purpose of the Study:

  • To develop a lightweight infrared object detection network for foggy environments.
  • To improve detection accuracy and real-time performance by addressing haze interference.

Main Methods:

  • Proposed IDDNet, a network integrating multi-scale fusion dehazing (MSFD).
  • Incorporated a dedicated dehazing loss function (DhLoss).
  • Utilized bidirectional polarized self-attention, a weighted bidirectional feature pyramid network, and multi-scale detection layers.

Main Results:

  • IDDNet achieved 89.4% precision and 83.9% AP on public datasets.
  • Demonstrated superior accuracy, processing speed, and generalization capabilities.
  • Showcased robust detection performance in foggy environments.

Conclusions:

  • IDDNet effectively mitigates haze interference in infrared images.
  • The proposed network offers a lightweight and efficient solution for object detection in adverse weather.
  • IDDNet enhances both accuracy and robustness for real-world applications.