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A Novel Approach for UAV Image Crack Detection.

Yanxiang Li1, Jinming Ma1, Ziyu Zhao1

  • 1College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.

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|May 20, 2022
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Summary

This study introduces DenxiDeepCrack, a deep learning model for automatic road crack detection using Unmanned Aerial Vehicle (UAV) imagery. A new dataset, UCrack 11, was also created to advance UAV-based road inspection research.

Keywords:
crack detectiondeeep learningimage stitchingtarget detectionunmanned aerial vehicle

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Remote Sensing

Background:

  • Road cracks are critical indicators of structural damage and safety hazards.
  • Current manual and vehicle-based detection methods are inefficient, unsafe, and disruptive.
  • Unmanned Aerial Vehicles (UAVs) offer a promising, efficient, and cost-effective alternative for road inspection.

Purpose of the Study:

  • To develop an automated road crack detection technique using UAV imagery.
  • To improve the efficiency and safety of road condition assessment.
  • To create a new dataset for advancing UAV-based crack detection research.

Main Methods:

  • Development of DenxiDeepCrack, a trainable deep convolutional neural network for automatic crack detection.
  • Utilizing high-level feature learning for accurate crack representation.
  • Collection and curation of a new drone imagery dataset named UCrack 11.

Main Results:

  • DenxiDeepCrack demonstrates effective automatic road crack detection from UAV images.
  • The UCrack 11 dataset provides a valuable resource for future research in this domain.
  • The proposed method shows potential for significant improvements in detection efficiency and economic benefits.

Conclusions:

  • UAV-based road crack detection using deep learning offers a viable and efficient solution.
  • DenxiDeepCrack and the UCrack 11 dataset represent significant advancements in automated road inspection.
  • This approach enhances safety and reduces disruption associated with traditional road assessment methods.