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Related Experiment Video

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UAV Landing Using Computer Vision Techniques for Human Detection.

David Safadinho1, João Ramos1, Roberto Ribeiro1

  • 1School of Technology and Management, Computer Science and Communication Research Centre, Polytechnic Institute of Leiria, Campus 2, Morro do Lena - Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal.

Sensors (Basel, Switzerland)
|January 26, 2020
PubMed
Summary

This study enhances drone delivery accuracy by integrating computer vision with GPS. A low-cost system uses convolutional neural networks for aerial human detection, improving landing site estimation without markers.

Keywords:
autonomous deliverycomputer visiondeep neural networksintelligent vehiclesinternet of thingsnext generation servicesreal-time systemsremote sensingunmanned aerial vehiclesunmanned aircraft systems

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Artificial Intelligence

Background:

  • Drones are increasingly used for retail deliveries, relying on Global Positioning System (GPS) for landing.
  • GPS accuracy is limited by environmental factors like tall buildings and terrain changes.
  • Existing drone delivery systems lack robust methods for verifying safe and accurate landing zones.

Purpose of the Study:

  • To improve the precision and safety of drone goods delivery.
  • To develop a system for detecting the potential human receiver for landing.
  • To enhance drone navigation by complementing GPS with computer vision.

Main Methods:

  • Developed a prototype integrating Global Positioning System (GPS) with Computer Vision (CV) algorithms.
  • Utilized Convolutional Neural Networks (CNNs) for aerial human detection.
  • Implemented the system on a Raspberry Pi 3 with a Pi NoIR Camera, testing Single Shot Detector (SSD) MobileNet-V2 and SSDLite-MobileNet-V2 models.

Main Results:

  • The SSDLite architecture achieved the best performance in afternoon tests.
  • Effective detection and landing position estimation were achieved at distances and heights between 2.5-10 meters.
  • Recalls ranged from 59% to 76%, demonstrating the system's capability.

Conclusions:

  • A cost-effective, low-power system can perform aerial human detection for drone deliveries.
  • Computer vision significantly enhances landing site accuracy beyond GPS limitations.
  • The developed system successfully estimates landing positions without requiring additional visual markers.