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A Protocol for Real-time 3D Single Particle Tracking
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SPR-YOLOv8: A Real-Time Instance Segmentation and Dynamic Size Measurement System for Diamond Particles.

Li Wang1, Hanwen Niu1, Tao Wang2

  • 1School of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

This study introduces a new framework for accurately measuring diamond particle size in real-time using video streams. The developed model significantly improves measurement accuracy and efficiency for industrial quality control.

Keywords:
diamond particlesinstance segmentationlightweight networkreal-time detection

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Area of Science:

  • Materials Science
  • Computer Vision
  • Industrial Automation

Background:

  • Traditional image processing methods struggle with complex industrial environments for diamond particle size measurement.
  • Existing deep learning models face challenges in balancing accuracy and efficiency for real-time applications.
  • Accurate and efficient diamond particle size analysis is crucial for quality control in superhard material manufacturing.

Purpose of the Study:

  • To propose an integrated framework for dynamic segmentation and morphological analysis of diamond particles from video streams.
  • To develop a lightweight and efficient deep learning model for real-time particle analysis.
  • To provide a robust solution for automated online quality control in diamond manufacturing.

Main Methods:

  • Construction of an automated data acquisition system with a motion stage, industrial camera, and microscope.
  • Development of a lightweight SPR-YOLOv8 instance segmentation model incorporating LSKA and RepBlock modules.
  • Introduction of a P2 small-object detection head for improved focus on tiny particles.
  • Application of a contour-based geometric analysis for particle size estimation.

Main Results:

  • The SPR-YOLOv8 model achieved an mAP@0.9 of 0.861 with a low parameter count (0.97 M) and high inference speed (500 FPS).
  • The proposed DPSCA framework reduced the mean absolute percentage error in particle size measurement by over 70% compared to conventional methods.
  • Demonstrated strong accuracy and stability in consecutive-frame tracking and robustness across different particle size ranges.

Conclusions:

  • The integrated framework offers a practical and efficient automated inspection solution for online quality control.
  • The developed deep learning model enhances accuracy and efficiency in dynamic particle size measurement.
  • The study provides a valuable tool for the superhard material manufacturing industry, particularly for diamond quality assessment.