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Related Experiment Video

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A Comprehensive Survey on Visual Perception Methods for Intelligent Inspection of High Dam Hubs.

Zhangjun Peng1,2, Li Li2,3, Daoguang Liu1,2

  • 1School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary

Intelligent inspection using visual perception technologies like UAVs and AI enhances high dam safety. This review covers image enhancement, defect detection, and damage quantification methods for more efficient and accurate dam monitoring.

Keywords:
deep learningdefects identificationdefects quantitative analysisenvironmental perceptionhigh damsimage enhancementsafety inspection

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • High dam safety inspections are critical for operational integrity.
  • Traditional manual inspections are inefficient, labor-intensive, and prone to subjective errors.

Purpose of the Study:

  • To review recent advancements in visual perception techniques for intelligent high dam inspection.
  • To categorize and analyze methods for image enhancement, defect detection, and damage quantification.

Main Methods:

  • Categorization of image enhancement techniques (histogram equalization, Retinex, deep learning).
  • Review of defect and obstacle perception methods (traditional image processing, machine learning).
  • Analysis of visual perception-based damage quantification methods.

Main Results:

  • Detailed elaboration of representative methods and their characteristics within each category.
  • Systematic enumeration of principal achievements in defect and obstacle perception.
  • Analysis of key techniques and characteristics for damage quantification.

Conclusions:

  • Intelligent inspection using visual perception is a viable and necessary advancement for high dam safety.
  • Current research shows significant progress in image enhancement and defect detection.
  • Future research should address challenges in applying these techniques for comprehensive intelligent safety inspections.