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Related Experiment Video

Updated: Jun 13, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Research on Multi-Type Rivet Head Defect Extraction and Classification Based on PointGhost Lightweight Network.

Liang Liu1,2, Wenxuan Zhou1,2, Xianming Meng3

  • 1Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces PointGhost, a lightweight network for detecting multi-type rivet head defects using 3D point clouds. It achieves high accuracy (99.49%) and efficiency, crucial for structural integrity in engineering inspections.

Area of Science:

  • Engineering
  • Computer Science
  • Materials Science

Background:

  • Rivet defects pose significant risks to structural integrity in aerospace, automotive, and civil engineering.
  • Current methods for detecting rivet head defects require improvement in feature extraction and classification performance.

Purpose of the Study:

  • To develop a lightweight classification network for detecting multi-type rivet head defects in 3D point clouds.
  • To enhance feature extraction and classification performance for improved defect detection.

Main Methods:

  • Utilized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for target head extraction and Non-Maximum Eigenvalue Curvature Method (NMECM) for data simplification.
  • Developed the PointGhost network featuring Virtual Block Sampling (VBS) for reduced computational complexity and Grouped Pointwise Convolution Ghost (GPC-Ghost) for feature learning.
Keywords:
3D point cloudfeature extractionlightweight networkrivet head defect

Related Experiment Videos

Last Updated: Jun 13, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

  • Integrated a Dynamic Screening Self-Attention (DSSA) mechanism to improve defect detection and employed Principal Component Analysis (PCA) and Total Least Squares (TLS) for severity quantification.

    • Main Results:

    • The GPC-Ghost model demonstrated superior performance, achieving 4.31% higher mean accuracy than PointNet++ with lower computational cost (0.66 GFLOPs).
    • The PointGhost model achieved a mean accuracy of 99.49% with an average misclassification rate of 1.19%, outperforming six other attention mechanisms and seven classification networks.
    • The proposed method effectively balances accuracy and efficiency in defect detection.

    Conclusions:

    • The PointGhost network offers a promising solution for accurate and efficient multi-type rivet head defect detection.
    • The developed method has significant potential for application in engineering quality inspection.
    • The study highlights the effectiveness of lightweight networks and attention mechanisms in complex 3D data analysis for structural integrity.