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Related Experiment Video

Updated: Jul 24, 2025

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review.

Munish Rathee1, Boris Bačić1, Maryam Doborjeh1,2

  • 1School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand.

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Summary
This summary is machine-generated.

This systematic review comprehensively analyzes computer vision applications for Automated Road Defect and Anomaly Detection (ARDAD). It identifies research gaps and trends to enhance traffic safety using advanced sensor technology.

Keywords:
ARDADcomputer visiondeep learningmachine learningmotorist safetyon-road anomaly detectionstructural damage detectiontransfer learning

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Area of Science:

  • Engineering
  • Computer Science
  • Transportation Science

Background:

  • Traffic-related injuries and fatalities necessitate advanced safety solutions.
  • Computer vision (CV) and sensor technology offer potential for mitigating road hazards.
  • Existing reviews lack a comprehensive investigation into CV for Automated Road Defect and Anomaly Detection (ARDAD).

Purpose of the Study:

  • To systematically review and present the state-of-the-art in CV applications for ARDAD.
  • To identify research gaps, challenges, and future implications in the field.
  • To consolidate popular open-access datasets and technology trends in ARDAD.

Main Methods:

  • Systematic literature review of 116 papers from 2000-2023.
  • Primary data sources: Scopus and Litmaps.
  • Analysis of research trends, datasets, and reported performance.

Main Results:

  • Identified key research gaps and challenges in ARDAD.
  • Cataloged 18 popular open-access datasets for ARDAD research.
  • Highlighted technology trends accelerating sensor technology application in ARDAD.

Conclusions:

  • The review provides a comprehensive overview of CV in ARDAD.
  • Identified artifacts will aid researchers in advancing traffic safety solutions.
  • Future research should focus on addressing identified gaps to improve road safety.