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Related Concept Videos

Microcracking in Concrete01:20

Microcracking in Concrete

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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
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Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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Pavement crack identification method based on IOtsu-Dd algorithm.

Yang Yang1, Lin Wang2, Qinghua Xiong1

  • 1School of Road and Bridge Engineering, Guangxi Transport Vocational and Technical College, Nanning, China.

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This study introduces an improved Otsu method for automatic highway crack recognition using unmanned aerial vehicle (UAV) images. The developed method significantly enhances accuracy and efficiency in identifying road cracks for better highway maintenance.

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Image Processing

Background:

  • Highway maintenance relies on timely identification of road cracks.
  • Unmanned aerial vehicles (UAVs) are increasingly used for road surface imaging.
  • Automatic crack recognition from UAV imagery is an active research area.

Purpose of the Study:

  • To develop an accurate and efficient method for road crack recognition using UAV images.
  • To improve upon existing Otsu-based segmentation techniques for pavement crack detection.
  • To construct a pavement crack recognition model utilizing damage density.

Main Methods:

  • Pre-processing of UAV images including grayscale conversion and equalization.
  • Image segmentation using an improved Otsu method for enhanced crack detection.
  • Development of a pavement crack recognition model incorporating damage density.

Main Results:

  • The developed method achieved an average accuracy of 98.2% in crack recognition.
  • The model demonstrated a recall rate of 0.75 and an F1 score of 0.85.
  • The proposed method effectively extracts and recognizes pavement crack features from UAV images.

Conclusions:

  • The improved Otsu method offers high effectiveness and accuracy for road crack recognition.
  • This approach provides a robust solution for automated highway maintenance using UAV technology.
  • The method successfully identifies pavement cracks from unmanned aerial vehicle captured images.