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Related Concept Videos

Lumber Defects01:23

Lumber Defects

201
Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
201

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Related Experiment Video

Updated: Sep 5, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Defects Recognition Algorithm Development from Visual UAV Inspections.

Nicolas P Avdelidis1, Antonios Tsourdos1, Pasquale Lafiosca1

  • 1School of Aerospace, Transport & Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.

Sensors (Basel, Switzerland)
|July 9, 2022
PubMed
Summary
This summary is machine-generated.

Automated aircraft skin defect detection using unmanned aerial vehicles (UAVs) and deep learning significantly improves safety. This AI-powered system automates visual inspections, reducing errors and costs associated with manual checks.

Keywords:
AICNNUAVaircraft inspectiondeep learningdefect classificationdefect recognition

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Area of Science:

  • Aerospace Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Manual aircraft skin inspection is time-consuming, costly, and prone to human error.
  • Automating visual inspection with unmanned aerial vehicles (UAVs) can enhance efficiency and safety.

Purpose of the Study:

  • To develop and evaluate a two-step automated process for aircraft defect recognition and classification from visual images.
  • To leverage deep learning, specifically convolutional neural networks (CNNs), for defect detection and categorization.

Main Methods:

  • Utilized a custom dataset of aircraft defect images captured by a UAV.
  • Employed transfer learning with pre-trained CNN architectures, including DenseNet201, for defect recognition.
  • Developed an ensemble of CNN models for defect classification.

Main Results:

  • DenseNet201 achieved 81.82% accuracy in defect recognition.
  • The ensemble model reached approximately 82% accuracy for defect recognition and up to 100% for specific defect classifications (paint, primer, dents).

Conclusions:

  • The proposed automated system effectively recognizes and classifies aircraft skin defects.
  • Deep learning models, even with limited data, show high potential for improving aircraft maintenance safety and efficiency.