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Infrared UAV Target Detection Based on Continuous-Coupled Neural Network.

Zhuoran Yang1, Jing Lian2, Jizhao Liu1

  • 1School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.

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|November 25, 2023
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Summary
This summary is machine-generated.

This study introduces a brain-inspired framework for detecting unmanned aerial vehicles (UAVs) in infrared images. The method enhances detection accuracy in challenging conditions, outperforming existing techniques.

Keywords:
CCNNUAV detectioninfrared image processing

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Infrared Imaging

Background:

  • Unmanned aerial vehicle (UAV) detection is crucial for security.
  • Infrared imaging offers advantages in complex environments but faces challenges like low signal-to-clutter ratio and limited data.
  • Traditional methods struggle with infrared UAV detection due to image noise and clutter.

Purpose of the Study:

  • To develop an effective framework for UAV detection in infrared images.
  • To address the limitations of existing methods in handling noisy and cluttered infrared data.
  • To leverage brain-inspired mechanisms for improved UAV detection.

Main Methods:

  • A novel framework inspired by human visual processing for UAV detection.
  • Utilizes a continuous-coupled neural network (CCNN) with parameters determined by image statistics.
  • Employs iterative pixel grouping, segmentation via expansion/erosion, and minimum circumscribed rectangle for final detection.

Main Results:

  • The proposed framework achieved superior performance compared to state-of-the-art brain-inspired methods.
  • Demonstrated a high average intersection over union (IoU) of 74.79%, with a maximum of 97.01% on UAV infrared images.
  • Effectively overcomes challenges of low signal-to-clutter and signal-to-noise ratios in infrared UAV detection.

Conclusions:

  • The brain-inspired framework provides an effective solution for infrared UAV detection.
  • The method shows significant improvements in detection accuracy and robustness.
  • Offers a promising approach for enhancing security through advanced aerial surveillance technology.