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This study presents a real-time drone detection system using video cameras and a convolutional neural network (CNN). The approach accurately identifies drones by first detecting moving objects and then classifying them, offering a cost-effective solution for security.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Increasing prevalence of drones necessitates advanced security systems.
  • Illegal drone usage poses significant security risks to guarded areas.
  • Existing drone detection methods face challenges with object similarity and high speeds.

Purpose of the Study:

  • To develop a real-time, high-accuracy drone detection system using visual information.
  • To address the challenge of distinguishing drones from similar objects like birds and airplanes.
  • To create a cost-effective drone detection solution.

Main Methods:

  • Implemented a two-stage approach: moving object detection and object classification.
  • Utilized background subtraction for initial moving object detection.
  • Employed a convolutional neural network (CNN) for classifying detected objects (drone, bird, background).

Main Results:

  • Achieved high accuracy in drone detection comparable to existing methods.
  • Demonstrated high processing speed suitable for real-time applications.
  • Validated the effectiveness of the combined background subtraction and CNN approach.

Conclusions:

  • The proposed method offers a viable and efficient solution for real-time drone detection.
  • The system's performance is robust but can be affected by moving backgrounds.
  • Further research could focus on mitigating the impact of dynamic backgrounds on detection accuracy.