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Related Experiment Video

Updated: Jun 29, 2025

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An Adaptive Radon-Transform-Based Marker Detection and Localization Method for Displacement Measurements Using

Jianlin Liu1, Wujiao Dai1, Yunsheng Zhang1

  • 1Department of Surveying Engineering & Geo-Informatics, Central South University, Changsha 410083, China.

Sensors (Basel, Switzerland)
|March 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive Radon transform method for precise detection of cross-shaped markers in Unmanned Aerial Vehicle (UAV) images, enhancing deformation monitoring accuracy. The new approach achieves a 97.2% detection rate and a 0.57-pixel error, outperforming existing techniques.

Keywords:
Radon transformUAV displacement measurementadaptive parametersmarker detection

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Area of Science:

  • Geomatics Engineering
  • Photogrammetry
  • Remote Sensing

Background:

  • Unmanned Aerial Vehicles (UAVs) offer flexible deformation monitoring but suffer from image quality issues affecting accuracy.
  • Existing methods struggle with rapid and precise detection of cross-shaped markers crucial for UAV-based monitoring.

Purpose of the Study:

  • To develop an adaptive Radon transform-based method for accurate detection and localization of cross-shaped markers in UAV imagery.
  • To improve the precision of UAV displacement measurements by overcoming limitations in marker detection.

Main Methods:

  • An adaptive Radon transform algorithm was developed, focusing on optimizing marker information acquisition radius and edge width parameters.
  • The method was tested under various flight altitudes, marker sizes, and parameter settings.

Main Results:

  • Achieved a marker detection rate of 97.2% across diverse experimental conditions.
  • Obtained a root mean square error of 0.57 pixels for marker detection and localization, significantly exceeding other methods.
  • Identified critical detection radii and optimal parameter combinations for different UAV altitudes.

Conclusions:

  • The proposed adaptive Radon transform method significantly enhances the accuracy and reliability of cross-shaped marker detection in UAV images.
  • This technique offers a practical solution for improving UAV-based deformation monitoring, particularly for displacement measurements.