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Area of Science:

  • Computer Vision
  • Metrology
  • Industrial Inspection

Background:

  • Accurate light stripe center extraction is crucial for line-structured light vision measurement.
  • Complex train wheel tread profiles cause uneven gray-level distribution and varying stripe widths, degrading measurement accuracy.
  • Existing methods struggle with the challenging surface characteristics of train wheel treads.

Purpose of the Study:

  • To propose a novel region-adaptive multiscale method for robust light stripe center extraction.
  • To address the challenges posed by diverse and complex train wheel tread profiles in online measurement.
  • To enhance the stability and accuracy of industrial measurement systems.

Main Methods:

  • Image segmentation using gray-gradient features to identify and enhance potential light stripe regions.
  • Adaptive determination of the optimal scale parameter (σ) for each region using normalized Gaussian kernel responses.
  • Sub-pixel light stripe center extraction via the Hessian matrix with the optimal scale parameter.

Main Results:

  • The proposed method reliably extracts light stripe centers with high stability on uneven wheel surfaces.
  • Achieved a repeatability of 0.10 mm for light stripe center extraction.
  • Demonstrated mean measurement errors of 0.12 mm for flange height and 0.10 mm for flange thickness.

Conclusions:

  • The region-adaptive multiscale method significantly enhances the stability and accuracy of light stripe center extraction.
  • The technique is validated for industrial measurement environments, particularly for train wheel tread analysis.
  • The method's repeatability and reproducibility were confirmed through extensive testing.