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Scrap-SAM-CLIP: Assembling Foundation Models for Typical Shape Recognition in Scrap Classification and Rating.

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Feasibility Study of Scrap Grading Systems Based on Three-Dimensional Vision Technology.

Guangda Bao1, Wenzhi Xia1, Yun Zhou1

  • 1School of Metallurgical Engineering, Anhui University of Technology, Ma'anshan 243032, China.

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Summary

This study introduces 3D vision for automated scrap grading, achieving 1 mm accuracy. A novel workflow using 3D reconstruction and Euclidean clustering significantly improves grading reliability over 2D methods.

Keywords:
3D vision technologymulti-view reconstructionpoint cloud segmentationscrap grading

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Area of Science:

  • Engineering
  • Computer Vision
  • Materials Science

Background:

  • Traditional manual scrap sorting is inefficient and prone to errors.
  • Automated grading systems are needed to improve accuracy and fairness.
  • 3D vision technology offers potential for enhanced scrap characterization.

Purpose of the Study:

  • To investigate the application of 3D vision technology for automated scrap grading.
  • To develop and evaluate an automated processing workflow for scrap grading using 3D data.
  • To compare the performance of different point cloud segmentation methods for scrap analysis.

Main Methods:

  • Multi-view 3D reconstruction algorithm for generating accurate scrap models.
  • Development of an automated pipeline integrating 3D reconstruction and point cloud segmentation.
  • Comparison of Euclidean clustering, Kmeans, DBSCAN, and Region Grow for point cloud segmentation.
  • Thickness measurement using 3D data for scrap characterization.

Main Results:

  • Multi-view 3D reconstruction achieved accuracy within 1 mm in synthetic and real scrap scenes.
  • Euclidean-clustering-based segmentation yielded the best trade-off, with an mIoU score of 99.35%.
  • Thickness measurement error was less than 0.5 mm, demonstrating high precision.
  • The proposed 3D vision workflow showed improved robustness and reliability compared to 2D image-based methods.

Conclusions:

  • 3D vision technology is suitable for accurate scrap grading, meeting precision requirements.
  • The developed automated workflow enhances the reliability and efficiency of scrap sorting.
  • 3D vision provides a robust foundation for future advancements in automated scrap grading systems.