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Attentional dual-stream interactive perception network for efficient infrared small aerial target detection.

Lihao Zhou1, Huawei Wang1

  • 1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, PR, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 16, 2026
PubMed
Summary

This study introduces the attentional dual-stream interactive perception network (ADIPNet) for improved infrared small target detection. ADIPNet enhances feature extraction, leading to more accurate identification of aerial targets in complex environments.

Keywords:
Context interactionCross attention mechanismDual-stream U-NetFeature detail extractionSmall aerial target detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Infrared Imaging Technology

Background:

  • Remote infrared imaging struggles with small aerial target feature detail loss and low detection efficiency.
  • Existing methods face challenges in deeply extracting features for small targets, especially under occlusion and environmental interference.

Purpose of the Study:

  • To propose an advanced deep learning network, the attentional dual-stream interactive perception network (ADIPNet), for enhanced infrared small target detection.
  • To address the limitations of insufficient feature extraction and low efficiency in current infrared small target identification systems.

Main Methods:

  • The proposed ADIPNet is based on a dual-stream U-Net architecture, incorporating multi-patch series-parallel attention (MSPA), edge anchoring with regret (EAR), context scene perception (CSP), and dual-stream interaction fusion (DSIF) modules.
  • MSPA mines global target information through multi-scale patch weighting and nested self-attention.
  • EAR refines edge detection, CSP enhances feature perception via context exchange, and DSIF fuses features using cross-attention for improved complex scenario understanding.

Main Results:

  • ADIPNet significantly alleviates insufficient feature extraction for infrared small targets.
  • The network achieved mean Intersection over Union (mIoU) scores of 80.52% and 72.54% on two large infrared datasets, outperforming state-of-the-art methods.
  • Demonstrated accurate detection of small aerial targets with low operational costs.

Conclusions:

  • ADIPNet offers a robust solution for infrared small target detection, improving accuracy and efficiency.
  • The network's ability to deeply extract features and understand complex scenarios shows significant potential for application in various infrared surveillance systems.