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Related Experiment Video

Updated: May 10, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems.

Joung-Hwan Yoon1, Chibuzo Nwabufo Okwuosa1, Nnamdi Chukwunweike Aronwora1

  • 1Department of Mechanical Engineering (Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology, 61 Daehak-ro, Gumi-si 39177, Gyeonsangbuk-do, Republic of Korea.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved AI method for detecting defects on glossy, curved products. A custom CNN model offers high accuracy and superior computational efficiency for industrial applications.

Keywords:
Dijkstra’s algorithmResNet-50VGG-16convolutional neural networkcurved surfacefault classificationfault detectionglossy surface

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Industrial Automation

Background:

  • Artificial intelligence (AI) is increasingly adopted in industry for its efficiency.
  • AI-powered image analysis is used for defect detection and quality control.
  • Traditional AI methods struggle with defect detection on glossy and curved surfaces due to reflectivity.

Purpose of the Study:

  • To develop an enhanced AI method for improved image data collection for defect detection on challenging surfaces.
  • To train and evaluate deep learning models for accurate and efficient fault detection.
  • To identify computationally robust and efficient AI models for industrial deployment.

Main Methods:

  • Utilized a Basler vision camera with specialized lighting and KEYENCE displacement sensors for enhanced image acquisition.
  • Trained eight deep learning algorithms, including custom Convolutional Neural Networks (CNNs), VGG-16, and ResNet-50 variations.
  • Employed image data from normal and two defect conditions for model training and evaluation.

Main Results:

  • ResNet-50224 achieved the highest accuracy (97.97%) with a loss of 0.1030.
  • CNN6-240 demonstrated superior computational efficiency with an average step time of 94 milliseconds and 95.08% accuracy.
  • The study identified trade-offs between overall accuracy and computational efficiency among the tested models.

Conclusions:

  • The enhanced data collection method improves AI defect detection on glossy, curved surfaces.
  • ResNet-50224 offers high accuracy, while CNN6-240 is suitable for resource-constrained environments.
  • The findings provide valuable insights for selecting appropriate AI models for industrial defect detection.