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DeepVision: Enhanced Drone Detection and Recognition in Visible Imagery through Deep Learning Networks.

Hassan J Al Dawasari1,2, Muhammad Bilal1,2, Muhammad Moinuddin1,2

  • 1Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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Summary

This study introduces a deep learning method to differentiate drones from birds, crucial for airport security. The SqueezeNet model achieved superior accuracy and speed for real-time drone detection.

Keywords:
artificial intelligenceclassificationdeep learningdrones detectiontransfer learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Drones pose significant risks to airport infrastructure due to misuse.
  • Unauthorized drone activity has repeatedly disrupted flight operations.
  • Distinguishing drones from birds is challenging due to similar appearances and flight patterns.

Purpose of the Study:

  • To develop and evaluate an innovative deep learning method for distinguishing drones from birds.
  • To address the challenge of detecting small drones and differentiating them from avian targets.
  • To improve the accuracy and efficiency of drone detection systems for aviation security.

Main Methods:

  • A novel deep learning approach was proposed for drone and bird classification.
  • A robust image-tiling technique with overlaps was developed to enhance detection of small drones.
  • Various convolutional neural network (CNN) models, including SqueezeNet, MobileNetV2, ResNet18, and ResNet50, were evaluated.
  • Performance was assessed using a dataset from the 2020 Drone vs. Bird Detection Challenge and an unseen dataset.

Main Results:

  • The SqueezeNet model demonstrated superior performance, achieving an average precision (AP) of 0.770 for medium area ratios.
  • The proposed method, particularly with SqueezeNet, surpassed existing drone detection systems in accuracy and speed.
  • The image-tiling technique significantly improved the detection of very small drones.
  • The system accurately identified and differentiated between drones and birds.

Conclusions:

  • The developed deep learning method offers a reliable solution for real-time drone detection and identification.
  • SqueezeNet is highly suitable for accurate and efficient drone detection in airport environments.
  • The approach enhances aviation security by mitigating risks associated with unauthorized drone presence.