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This study introduces Scrap-SAM-CLIP (SSC), a vision-language model for accurate 3D scrap dimension inference. SSC enhances industrial vision systems by improving scrap shape identification and 3D reconstruction capabilities.

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3D recognition solutioncontrastive language-image pre-trainingfine-tuningscrap identificationsegment anything model

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Industrial Automation

Background:

  • 2D methods struggle with inferring absolute scrap dimensions from images.
  • Accurate 3D recognition requires robust scrap shape identification and dimensional extraction.

Purpose of the Study:

  • To develop a vision-language model for precise 3D scrap dimension inference.
  • To establish a foundational framework for 3D reconstruction and dimensional extraction in industrial scrap identification.

Main Methods:

  • Proposed Scrap-SAM-CLIP (SSC), integrating Segment Anything Model (SAM) and Chinese Contrastive Language-Image Pre-training (CN-CLIP).
  • Fine-tuned individual SSC modules on a self-constructed scrap dataset.
  • Evaluated various prompting strategies for segmentation, with combined box-and-point prompts showing optimal performance.

Main Results:

  • SSC achieved 95.3% accuracy in classification, outperforming Swin Transformer V2_base by 2.9%.
  • Fine-tuning the SAM decoder improved robustness under noisy prompts, increasing accuracy by up to 15%.
  • t-SNE visualizations confirmed SSC's superior feature learning capabilities.

Conclusions:

  • The modular assembly strategy of SSC enables component-specific optimization and enhances system interpretability.
  • Adapted foundation models prove effective for industrial vision systems, refining the scrap 3D identification pipeline.