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Integrated Video and Acoustic Emission Data Fusion for Intelligent Decision Making in Material Surface Inspection

Andrey V Chernov1, Ilias K Savvas2, Alexander A Alexandrov1

  • 1The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia.

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Summary

This study introduces a data integration strategy for intelligent surface inspection systems. By fusing RGB, RGB-D images, and acoustic emission signals, it enhances defect detection in metal welds.

Keywords:
acoustic emission controlcomputer visiondata fusiondecision makingintelligent systemsurface inspection

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Area of Science:

  • Intelligent Systems
  • Computer Vision
  • Signal Processing

Background:

  • Intelligent surface inspection systems rely on sensor data for decision-making.
  • Integrating diverse data sources enhances object awareness and decision accuracy.

Purpose of the Study:

  • To present an intelligent decision-making approach using data integration for surface defect detection.
  • To apply this approach to identify defects in metal pipe welds.

Main Methods:

  • Utilized multimodality data: RGB images, RGB-D images, and acoustic emission signals.
  • Employed computer vision and digital signal processing techniques to mimic human sensory perception.
  • Developed a data fusion strategy to combine information from different sensors.

Main Results:

  • Detailed system architecture and data acquisition methods are presented.
  • Provided pseudocodes for data processing algorithms.
  • Demonstrated an effective data fusion approach for improved decision-making in defect detection.

Conclusions:

  • The proposed data integration strategy enhances decision-making in intelligent surface inspection.
  • Fusion of visual and acoustic data improves the efficiency and accuracy of weld defect detection.