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Aircraft Position Estimation Using Deep Convolutional Neural Networks for Low SNR (Signal-to-Noise Ratio) Values.

Przemyslaw Mazurek1, Wojciech Chlewicki1

  • 1Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, al. Piastow 17, 70-310 Szczecin, Poland.

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Summary

Convolutional Neural Networks (ConvNN) improve aircraft detection in challenging conditions. This advanced method enhances airspace safety by accurately tracking small, noisy aircraft images.

Keywords:
air surveillancedeep convolutional neural networksimage processingmachine vision

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Airspace safety relies on effective aircraft detection and tracking.
  • Small aircraft size and high image noise pose significant challenges for traditional visual methods.
  • Existing methods struggle with detecting small targets obscured by background noise.

Purpose of the Study:

  • To develop an improved method for detecting and tracking small aircraft in noisy images.
  • To evaluate the performance of Convolutional Neural Networks (ConvNN) against traditional algorithms for aircraft segmentation.
  • To enhance the reliability of visual surveillance systems for airspace safety.

Main Methods:

  • Utilized Convolutional Neural Networks (ConvNN) for aircraft image segmentation.
  • Trained the ConvNN model using a database of actual aircraft images.
  • Compared ConvNN's performance against four Max algorithms (Pixel Value, Min. Pixel Value, Max. Abs. Pixel Value) using Monte Carlo simulations.
  • Employed deep dream analysis to understand ConvNN's feature preferences.
  • Integrated processed image values with raw data using the Track-Before-Detect method for tracking.

Main Results:

  • ConvNN demonstrated superior detection performance compared to the Max algorithms.
  • For a standard deviation of 0.1, ConvNN's detection was twice as effective.
  • Deep dream analysis revealed ConvNN's preference for horizontal contrast lines in images.
  • The proposed Track-Before-Detect method, using ConvNN outputs, proved effective for tracking.

Conclusions:

  • ConvNN offers a robust solution for detecting small, noisy aircraft, significantly improving upon traditional methods.
  • The developed system enhances visual surveillance capabilities for critical airspace monitoring.
  • This approach contributes to advancing automated systems for aviation safety and security.