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A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles.

Dario Cazzato1, Claudio Cimarelli1, Jose Luis Sanchez-Lopez1

  • 1Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg.

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Summary

This survey reviews 2D object detection for Unmanned Aerial Vehicles (UAVs). It highlights adaptations for UAV operations and proposes a new taxonomy based on methodological approaches.

Keywords:
2d object detectioncomputer visiondeep learningunmanned aerial vehicles

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Area of Science:

  • Computer Vision
  • Robotics
  • Remote Sensing

Background:

  • Unmanned Aerial Vehicles (UAVs) have revolutionized various fields, necessitating advancements in autonomous operations.
  • Object detection is a critical low-level task, with RGB cameras being the most common sensors due to cost and availability.
  • Existing research often focuses on generic object detection, with less emphasis on adaptations specific to UAV platforms.

Purpose of the Study:

  • To survey recent advancements in 2D object detection tailored for UAV applications.
  • To analyze the differences, strategies, and trade-offs in adapting generic object detection methods for UAVs.
  • To introduce a novel taxonomy for UAV-based object detection, categorized by height intervals and methodological approaches.

Main Methods:

  • Comprehensive literature review of state-of-the-art 2D object detection techniques for UAVs.
  • Comparative analysis of generic object detection versus UAV-specific adaptations.
  • Development of a new classification taxonomy based on methodological strategies and operational height.

Main Results:

  • Identified key challenges and advancements in UAV-based 2D object detection.
  • Highlighted the specific considerations and trade-offs for deploying object detection on UAVs.
  • Established a novel taxonomy to better categorize and understand research in this domain.

Conclusions:

  • The adaptation of 2D object detection for UAVs presents unique challenges and requires specialized strategies.
  • The proposed taxonomy offers a new framework for analyzing and advancing research in UAV-based object detection.
  • Further research should focus on methodologically driven approaches for diverse UAV operational heights.